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Revert "Merge branch 'master' of https://github.com/qurator-spk/sbb_textline_detector"

Browse source 
This reverts commit 2c89bf3b35ee290d7b830ef270df3a96aa48245e, reversing
changes made to 9f7e413148ca5dbac9b555d7b0d0a5fa3a0f5340.
This commit is contained in:
Gerber, Mike 2019-12-09 12:44:05 +01:00
parent 1303a7d92f
commit 48a31ce672
73 changed files with 64834 additions and 1686 deletions
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3
qurator/__init__.py
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@ -1 +1,2 @@
__import__('pkg_resources').declare_namespace(__name__)
__import__('pkg_resources').declare_namespace(__name__)

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qurator/dinglehopper/.gitignore vendored Normal file
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# User-specific stuff
.idea/**/workspace.xml
.idea/**/tasks.xml
.idea/**/usage.statistics.xml
.idea/**/dictionaries
.idea/**/shelf

12
qurator/dinglehopper/.idea/dinglehopper.iml generated Normal file
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<?xml version="1.0" encoding="UTF-8"?>
<module type="PYTHON_MODULE" version="4">
<component name="NewModuleRootManager">
<content url="file://$MODULE_DIR$" />
<orderEntry type="jdk" jdkName="Python 3.7 (dinglehopper)" jdkType="Python SDK" />
<orderEntry type="sourceFolder" forTests="false" />
</component>
<component name="TestRunnerService">
<option name="projectConfiguration" value="pytest" />
<option name="PROJECT_TEST_RUNNER" value="pytest" />
</component>
</module>

7
qurator/dinglehopper/.idea/misc.xml generated Normal file
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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.7 (dinglehopper)" project-jdk-type="Python SDK" />
<component name="PyCharmProfessionalAdvertiser">
<option name="shown" value="true" />
</component>
</project>

8
qurator/dinglehopper/.idea/modules.xml generated Normal file
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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="ProjectModuleManager">
<modules>
<module fileurl="file://$PROJECT_DIR$/.idea/dinglehopper.iml" filepath="$PROJECT_DIR$/.idea/dinglehopper.iml" />
</modules>
</component>
</project>

5
qurator/dinglehopper/__init__.py Normal file
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from .ocr_files import *
from .substitute_equivalences import *
from .character_error_rate import *
from .word_error_rate import *
from .align import *

43
qurator/dinglehopper/align.py Normal file
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from .edit_distance import *
def align(t1, t2):
"""Align text."""
s1 = list(grapheme_clusters(unicodedata.normalize('NFC', t1)))
s2 = list(grapheme_clusters(unicodedata.normalize('NFC', t2)))
return seq_align(s1, s2)
def seq_align(s1, s2):
"""Align general sequences."""
s1 = list(s1)
s2 = list(s2)
ops = seq_editops(s1, s2)
i = 0
j = 0
while i < len(s1) or j < len(s2):
o = None
try:
ot = ops[0]
if ot[1] == i and ot[2] == j:
ops = ops[1:]
o = ot
except IndexError:
pass
if o:
if o[0] == 'insert':
yield (None, s2[j])
j += 1
elif o[0] == 'delete':
yield (s1[i], None)
i += 1
elif o[0] == 'replace':
yield (s1[i], s2[j])
i += 1
j += 1
else:
yield (s1[i], s2[j])
i += 1
j += 1

21
qurator/dinglehopper/character_error_rate.py Normal file
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from __future__ import division
import unicodedata
from uniseg.graphemecluster import grapheme_clusters
from qurator.dinglehopper.edit_distance import distance
def character_error_rate(reference, compared):
d = distance(reference, compared)
if d == 0:
return 0
n = len(list(grapheme_clusters(unicodedata.normalize('NFC', reference))))
if n == 0:
return float('inf')
return d/n
# XXX Should we really count newlines here?

106
qurator/dinglehopper/cli.py Normal file
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import os
import click
from jinja2 import Environment, FileSystemLoader
from markupsafe import escape
from qurator.dinglehopper import *
def gen_diff_report(gt_things, ocr_things, css_prefix, joiner, none, align):
gtx = ''
ocrx = ''
def format_thing(t, css_classes=None):
if t is None:
html_t = none
css_classes += ' ellipsis'
elif t == '\n':
html_t = '<br>'
else:
html_t = escape(t)
if css_classes:
return '<span class="{css_classes}">{html_t}</span>'.format(css_classes=css_classes, html_t=html_t)
else:
return '{html_t}'.format(html_t=html_t)
for k, (g, o) in enumerate(align(gt_things, ocr_things)):
if g == o:
css_classes = None
else:
css_classes = '{css_prefix}diff{k} diff'.format(css_prefix=css_prefix, k=k)
gtx += joiner + format_thing(g, css_classes)
ocrx += joiner + format_thing(o, css_classes)
return \
'''
<div class="row">
<div class="col-md-6 gt">{}</div>
<div class="col-md-6 ocr">{}</div>
</div>
'''.format(gtx, ocrx)
def process(gt, ocr, report_prefix):
"""Check OCR result against GT.
The @click decorators change the signature of the decorated functions, so we keep this undecorated version and use
Click on a wrapper.
"""
gt_text = text(gt)
ocr_text = text(ocr)
gt_text = substitute_equivalences(gt_text)
ocr_text = substitute_equivalences(ocr_text)
cer = character_error_rate(gt_text, ocr_text)
wer = word_error_rate(gt_text, ocr_text)
char_diff_report = gen_diff_report(gt_text, ocr_text, css_prefix='c', joiner='', none='·', align=align)
gt_words = words_normalized(gt_text)
ocr_words = words_normalized(ocr_text)
word_diff_report = gen_diff_report(gt_words, ocr_words, css_prefix='w', joiner=' ', none='⋯', align=seq_align)
def json_float(value):
"""Convert a float value to an JSON float.
This is here so that float('inf') yields "Infinity", not "inf".
"""
if value == float('inf'):
return 'Infinity'
elif value == float('-inf'):
return '-Infinity'
else:
return str(value)
env = Environment(loader=FileSystemLoader(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'templates')))
env.filters['json_float'] = json_float
for report_suffix in ('.html', '.json'):
template_fn = 'report' + report_suffix + '.j2'
out_fn = report_prefix + report_suffix
template = env.get_template(template_fn)
template.stream(
gt=gt, ocr=ocr,
cer=cer, wer=wer,
char_diff_report=char_diff_report,
word_diff_report=word_diff_report
).dump(out_fn)
@click.command()
@click.argument('gt', type=click.Path(exists=True))
@click.argument('ocr', type=click.Path(exists=True))
@click.argument('report_prefix', type=click.Path(), default='report')
def main(gt, ocr, report_prefix):
process(gt, ocr, report_prefix)
if __name__ == '__main__':
main()

122
qurator/dinglehopper/edit_distance.py Normal file
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from __future__ import division, print_function
import unicodedata
from functools import partial, lru_cache
from typing import Sequence, Tuple
import numpy as np
from uniseg.graphemecluster import grapheme_clusters
def levenshtein_matrix(seq1: Sequence, seq2: Sequence):
"""Compute the matrix commonly computed to produce the Levenshtein distance.
This is also known as the Wagner-Fischer algorithm. The matrix element at the bottom right contains the desired
edit distance.
This algorithm is implemented here because we need an implementation that can work with sequences other than
strings, e.g. lists of grapheme clusters or lists of word strings.
"""
# Internally, we use a cached version. As the cache only works on hashable parameters, we convert the input
# sequences to tuples to make them hashable.
return _levenshtein_matrix(tuple(seq1), tuple(seq2))
@lru_cache(maxsize=10)
def _levenshtein_matrix(seq1: Tuple, seq2: Tuple):
"""Compute the matrix commonly computed to produce the Levenshtein distance.
This is a LRU cached function not meant to be used directly. Use levenshtein_matrix() instead.
"""
m = len(seq1)
n = len(seq2)
def from_to(start, stop):
return range(start, stop + 1, 1)
D = np.zeros((m + 1, n + 1), np.int)
D[0, 0] = 0
for i in from_to(1, m):
D[i, 0] = i
for j in from_to(1, n):
D[0, j] = j
for i in from_to(1, m):
for j in from_to(1, n):
D[i, j] = min(
D[i - 1, j - 1] + 1 * (seq1[i - 1] != seq2[j - 1]), # Same or Substitution
D[i, j - 1] + 1, # Insertion
D[i - 1, j] + 1 # Deletion
)
return D
def levenshtein(seq1, seq2):
"""Compute the Levenshtein edit distance between two sequences"""
m = len(seq1)
n = len(seq2)
D = levenshtein_matrix(seq1, seq2)
return D[m, n]
def levenshtein_matrix_cache_clear():
"""Clear internal Levenshtein matrix cache.
You want to do this between different input file pairs to decrease memory
usage by not caching results from prior input files.
"""
_levenshtein_matrix.cache_clear()
def distance(s1, s2):
"""Compute the Levenshtein edit distance between two Unicode strings
Note that this is different from levenshtein() as this function knows about Unicode normalization and grapheme
clusters. This should be the correct way to compare two Unicode strings.
"""
s1 = list(grapheme_clusters(unicodedata.normalize('NFC', s1)))
s2 = list(grapheme_clusters(unicodedata.normalize('NFC', s2)))
return levenshtein(s1, s2)
def seq_editops(seq1, seq2):
"""
Return sequence of edit operations transforming one sequence to another.
This aims to return the same/similar results as python-Levenshtein's editops(), just generalized to arbitrary
sequences.
"""
seq1 = list(seq1)
seq2 = list(seq2)
m = len(seq1)
n = len(seq2)
D = levenshtein_matrix(seq1, seq2)
def _tail_backtrace(i, j, accumulator):
if i > 0 and D[i - 1, j] + 1 == D[i, j]:
return partial(_tail_backtrace, i - 1, j, [('delete', i-1, j)] + accumulator)
if j > 0 and D[i, j - 1] + 1 == D[i, j]:
return partial(_tail_backtrace, i, j - 1, [('insert', i, j-1)] + accumulator)
if i > 0 and j > 0 and D[i - 1, j - 1] + 1 == D[i, j]:
return partial(_tail_backtrace, i - 1, j - 1, [('replace', i-1, j-1)] + accumulator)
if i > 0 and j > 0 and D[i - 1, j - 1] == D[i, j]:
return partial(_tail_backtrace, i - 1, j - 1, accumulator) # NOP
return accumulator
def backtrace(i, j):
result = partial(_tail_backtrace, i, j, [])
while isinstance(result, partial):
result = result()
return result
b = backtrace(m, n)
return b
def editops(word1, word2):
# XXX Note that this returns indices to the _grapheme clusters_, not characters!
word1 = list(grapheme_clusters(unicodedata.normalize('NFC', word1)))
word2 = list(grapheme_clusters(unicodedata.normalize('NFC', word2)))
return seq_editops(word1, word2)

1037
qurator/dinglehopper/notebooks/Levenshtein.ipynb Normal file
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File diff suppressed because it is too large Load diff

558
qurator/dinglehopper/notebooks/Unicode normalization and Character segmentation.ipynb Normal file
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import unicodedata"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def list_characters(s):\n",
" \"\"\"List characters of string s, as seen by Python\"\"\"\n",
" for c in s:\n",
" print(c, end=' ')\n",
" if unicodedata.combining(c):\n",
" print(end=' ')\n",
" print(unicodedata.name(c))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Comparing two Unicode strings"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"S LATIN CAPITAL LETTER S\n",
"c LATIN SMALL LETTER C\n",
"h LATIN SMALL LETTER H\n",
"l LATIN SMALL LETTER L\n",
"y LATIN SMALL LETTER Y\n",
"ñ LATIN SMALL LETTER N WITH TILDE\n",
"\n",
"S LATIN CAPITAL LETTER S\n",
"c LATIN SMALL LETTER C\n",
"h LATIN SMALL LETTER H\n",
"l LATIN SMALL LETTER L\n",
"y LATIN SMALL LETTER Y\n",
"n LATIN SMALL LETTER N\n",
"̃ COMBINING TILDE\n",
"\n"
]
}
],
"source": [
"words = [unicodedata.normalize('NFC', 'Schlyñ'), unicodedata.normalize('NFD', 'Schlyñ')]\n",
"\n",
"for s in words:\n",
" list_characters(s)\n",
" print()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These two strings are different:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"words[0] == words[1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And yet they are the canonically equivalent:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unicodedata.normalize('NFC', words[0]) == unicodedata.normalize('NFC', words[1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"→ Normalize to NFC (Normalization Form Composed) to compare. NFC is also composed, which is what we want. But it doesn't matter because we're not interested in the characters as Python sees them, but in grapheme clusters (see below.)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Grapheme clusters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For evaluation we're interesting in what is perceived as \"characters\". But is \"ñ\" 1 character (LATIN SMALL LETTER N WITH TILDE) or 2 (LATIN SMALL LETTER N + COMBINING TILDE)?\n",
"\n",
"What we're probably want are [grapheme clusters](https://uniseg-python.readthedocs.io/en/latest/graphemecluster.html):"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['S', 'c', 'h', 'l', 'y', 'ñ']\n",
"['S', 'c', 'h', 'l', 'y', 'ñ']\n"
]
}
],
"source": [
"from uniseg.graphemecluster import grapheme_clusters\n",
"\n",
"for w in words:\n",
" print(list(grapheme_clusters(w)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Just looking at the interesting character – the last one - from both words:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ñ LATIN SMALL LETTER N WITH TILDE\n",
"\n",
"n LATIN SMALL LETTER N\n",
"̃ COMBINING TILDE\n",
"\n"
]
}
],
"source": [
"for w in words:\n",
" list_characters(list(grapheme_clusters(w))[-1])\n",
" print()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"→ Work with grapheme clusters, not \"characters as Python sees them\"."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"def unicode_name(c):\n",
" if 0xE000 <= ord(c) <= 0xF8FF:\n",
" return 'private use character 0x{:04X}'.format(ord(c))\n",
" else:\n",
" return unicodedata.name(c)\n",
" \n",
"\n",
"def list_grapheme_clusters(s):\n",
" \"\"\"List grapheme clusters of string s\"\"\"\n",
" for g in grapheme_clusters(s):\n",
" print(g, end=' ')\n",
" if len(g) > 1:\n",
" print('(multiple)', end=' ')\n",
" try:\n",
" print(', '.join(unicode_name(c) for c in g))\n",
" except ValueError:\n",
" print('ValueError')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"S LATIN CAPITAL LETTER S\n",
"c LATIN SMALL LETTER C\n",
"h LATIN SMALL LETTER H\n",
"l LATIN SMALL LETTER L\n",
"y LATIN SMALL LETTER Y\n",
"ñ LATIN SMALL LETTER N WITH TILDE\n",
"\n",
"S LATIN CAPITAL LETTER S\n",
"c LATIN SMALL LETTER C\n",
"h LATIN SMALL LETTER H\n",
"l LATIN SMALL LETTER L\n",
"y LATIN SMALL LETTER Y\n",
"ñ (multiple) LATIN SMALL LETTER N, COMBINING TILDE\n",
"\n"
]
}
],
"source": [
"for w in words:\n",
" list_grapheme_clusters(w)\n",
" print()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"私 CJK UNIFIED IDEOGRAPH-79C1\n",
"は HIRAGANA LETTER HA\n",
"彼 CJK UNIFIED IDEOGRAPH-5F7C\n",
"女 CJK UNIFIED IDEOGRAPH-5973\n",
"が HIRAGANA LETTER GA\n",
"お HIRAGANA LETTER O\n",
"茶 CJK UNIFIED IDEOGRAPH-8336\n",
"を HIRAGANA LETTER WO\n",
"好 CJK UNIFIED IDEOGRAPH-597D\n",
"き HIRAGANA LETTER KI\n",
"な HIRAGANA LETTER NA\n",
"事 CJK UNIFIED IDEOGRAPH-4E8B\n",
"が HIRAGANA LETTER GA\n",
"分 CJK UNIFIED IDEOGRAPH-5206\n",
"か HIRAGANA LETTER KA\n",
"っ HIRAGANA LETTER SMALL TU\n",
"た HIRAGANA LETTER TA\n",
"。 IDEOGRAPHIC FULL STOP\n"
]
}
],
"source": [
"list_grapheme_clusters('私は彼女がお茶を好きな事が分かった。')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
". FULL STOP\n",
" SPACE\n",
"ا ARABIC LETTER ALEF\n",
"م ARABIC LETTER MEEM\n",
"ا ARABIC LETTER ALEF\n",
" SPACE\n",
"چ ARABIC LETTER TCHEH\n",
"ن ARABIC LETTER NOON\n",
"د ARABIC LETTER DAL\n",
" SPACE\n",
"ت ARABIC LETTER TEH\n",
"ا ARABIC LETTER ALEF\n",
" SPACE\n",
"ح ARABIC LETTER HAH\n",
"ر ARABIC LETTER REH\n",
"ف ARABIC LETTER FEH\n",
" SPACE\n",
"ت ARABIC LETTER TEH\n",
"و ARABIC LETTER WAW\n",
" SPACE\n",
"ف ARABIC LETTER FEH\n",
"ا ARABIC LETTER ALEF\n",
"ر ARABIC LETTER REH\n",
"س ARABIC LETTER SEEN\n",
"ی ARABIC LETTER FARSI YEH\n",
" SPACE\n",
"ه ARABIC LETTER HEH\n",
"س ARABIC LETTER SEEN\n",
"ت ARABIC LETTER TEH\n",
" SPACE\n",
"ک ARABIC LETTER KEHEH\n",
"ه ARABIC LETTER HEH\n",
" SPACE\n",
"ت ARABIC LETTER TEH\n",
"و ARABIC LETTER WAW\n",
" SPACE\n",
"ع ARABIC LETTER AIN\n",
"ر ARABIC LETTER REH\n",
"ب ARABIC LETTER BEH\n",
"ی ARABIC LETTER FARSI YEH\n",
" SPACE\n",
"ن ARABIC LETTER NOON\n",
"ی ARABIC LETTER FARSI YEH\n",
"س ARABIC LETTER SEEN\n",
"ت ARABIC LETTER TEH\n"
]
}
],
"source": [
"list_grapheme_clusters('. اما چند تا حرف تو فارسی هست که تو عربی نیست')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
". FULL STOP\n",
" SPACE\n",
"ل ARABIC LETTER LAM\n",
"ك ARABIC LETTER KAF\n",
"ن ARABIC LETTER NOON\n",
" SPACE\n",
"ك ARABIC LETTER KAF\n",
"م ARABIC LETTER MEEM\n",
" SPACE\n",
"ع ARABIC LETTER AIN\n",
"د ARABIC LETTER DAL\n",
"د ARABIC LETTER DAL\n",
" SPACE\n",
"ا ARABIC LETTER ALEF\n",
"ل ARABIC LETTER LAM\n",
"ك ARABIC LETTER KAF\n",
"ل ARABIC LETTER LAM\n",
"م ARABIC LETTER MEEM\n",
"ا ARABIC LETTER ALEF\n",
"ت ARABIC LETTER TEH\n",
" SPACE\n",
"ب ARABIC LETTER BEH\n",
"ا ARABIC LETTER ALEF\n",
"ل ARABIC LETTER LAM\n",
"ف ARABIC LETTER FEH\n",
"ا ARABIC LETTER ALEF\n",
"ر ARABIC LETTER REH\n",
"س ARABIC LETTER SEEN\n",
"ي ARABIC LETTER YEH\n",
"ة ARABIC LETTER TEH MARBUTA\n",
" SPACE\n",
"ه ARABIC LETTER HEH\n",
"ل ARABIC LETTER LAM\n",
" SPACE\n",
"أ ARABIC LETTER ALEF WITH HAMZA ABOVE\n",
"ن ARABIC LETTER NOON\n",
"ت ARABIC LETTER TEH\n",
" SPACE\n",
"ب ARABIC LETTER BEH\n",
"ا ARABIC LETTER ALEF\n",
"ل ARABIC LETTER LAM\n",
"ل ARABIC LETTER LAM\n",
"غ ARABIC LETTER GHAIN\n",
"ة ARABIC LETTER TEH MARBUTA\n",
" SPACE\n",
"ا ARABIC LETTER ALEF\n",
"ل ARABIC LETTER LAM\n",
"ع ARABIC LETTER AIN\n",
"ر ARABIC LETTER REH\n",
"ب ARABIC LETTER BEH\n",
"ي ARABIC LETTER YEH\n",
"ة ARABIC LETTER TEH MARBUTA\n",
"؟ ARABIC QUESTION MARK\n"
]
}
],
"source": [
"list_grapheme_clusters('. لكن كم عدد الكلمات بالفارسية هل أنت باللغة العربية؟')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"H LATIN CAPITAL LETTER H\n",
"e LATIN SMALL LETTER E\n",
"l LATIN SMALL LETTER L\n",
"l LATIN SMALL LETTER L\n",
"😀 GRINNING FACE\n",
" SPACE\n",
"W LATIN CAPITAL LETTER W\n",
"😀 GRINNING FACE\n",
"r LATIN SMALL LETTER R\n",
"l LATIN SMALL LETTER L\n",
"d LATIN SMALL LETTER D\n",
"! EXCLAMATION MARK\n"
]
}
],
"source": [
"list_grapheme_clusters('Hell😀 W😀rld!')"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"u̶̜͓̬̞͚͙̪̰͓̯̲̝̬͔͎̳̼͇̓͊ͤ̋̃̀̄̓̿͊̀̚͟͜͟ͅ (multiple) LATIN SMALL LETTER U, COMBINING COMMA ABOVE, COMBINING NOT TILDE ABOVE, COMBINING LATIN SMALL LETTER E, COMBINING DOUBLE ACUTE ACCENT, COMBINING TILDE, COMBINING GRAVE ACCENT, COMBINING LEFT ANGLE ABOVE, COMBINING MACRON, COMBINING COMMA ABOVE, COMBINING DOUBLE OVERLINE, COMBINING NOT TILDE ABOVE, COMBINING DOUBLE MACRON BELOW, COMBINING GRAVE TONE MARK, COMBINING DOUBLE BREVE BELOW, COMBINING LONG STROKE OVERLAY, COMBINING DOUBLE MACRON BELOW, COMBINING LEFT HALF RING BELOW, COMBINING X BELOW, COMBINING CARON BELOW, COMBINING DOWN TACK BELOW, COMBINING DOUBLE RING BELOW, COMBINING ASTERISK BELOW, COMBINING BRIDGE BELOW, COMBINING TILDE BELOW, COMBINING X BELOW, COMBINING INVERTED BREVE BELOW, COMBINING LOW LINE, COMBINING UP TACK BELOW, COMBINING CARON BELOW, COMBINING LEFT ARROWHEAD BELOW, COMBINING UPWARDS ARROW BELOW, COMBINING DOUBLE LOW LINE, COMBINING SEAGULL BELOW, COMBINING EQUALS SIGN BELOW, COMBINING GREEK YPOGEGRAMMENI\n",
"ņ̷͔̤̜̗̘̠̦̦̖̟͉̹͕̬͎̙̲̲̎̅̈́ͮͣ̔̀̌͂̄͆͑̚ (multiple) LATIN SMALL LETTER N, COMBINING DOUBLE VERTICAL LINE ABOVE, COMBINING OVERLINE, COMBINING GREEK DIALYTIKA TONOS, COMBINING LEFT ANGLE ABOVE, COMBINING LATIN SMALL LETTER V, COMBINING LATIN SMALL LETTER A, COMBINING REVERSED COMMA ABOVE, COMBINING GRAVE ACCENT, COMBINING CARON, COMBINING GREEK PERISPOMENI, COMBINING MACRON, COMBINING BRIDGE ABOVE, COMBINING LEFT HALF RING ABOVE, COMBINING SHORT SOLIDUS OVERLAY, COMBINING CEDILLA, COMBINING LEFT ARROWHEAD BELOW, COMBINING DIAERESIS BELOW, COMBINING LEFT HALF RING BELOW, COMBINING ACUTE ACCENT BELOW, COMBINING LEFT TACK BELOW, COMBINING MINUS SIGN BELOW, COMBINING COMMA BELOW, COMBINING COMMA BELOW, COMBINING GRAVE ACCENT BELOW, COMBINING PLUS SIGN BELOW, COMBINING LEFT ANGLE BELOW, COMBINING RIGHT HALF RING BELOW, COMBINING RIGHT ARROWHEAD BELOW, COMBINING CARON BELOW, COMBINING UPWARDS ARROW BELOW, COMBINING RIGHT TACK BELOW, COMBINING LOW LINE, COMBINING LOW LINE\n",
"i̴̢͖̳̣̙͕̍ͯͧ̀ͥͭ̆ͣ̉͐͆̊͋͛̈́͒͟ (multiple) LATIN SMALL LETTER I, COMBINING VERTICAL LINE ABOVE, COMBINING LATIN SMALL LETTER X, COMBINING LATIN SMALL LETTER U, COMBINING GRAVE ACCENT, COMBINING LATIN SMALL LETTER I, COMBINING LATIN SMALL LETTER T, COMBINING BREVE, COMBINING LATIN SMALL LETTER A, COMBINING HOOK ABOVE, COMBINING RIGHT ARROWHEAD ABOVE, COMBINING BRIDGE ABOVE, COMBINING RING ABOVE, COMBINING HOMOTHETIC ABOVE, COMBINING ZIGZAG ABOVE, COMBINING GREEK DIALYTIKA TONOS, COMBINING FERMATA, COMBINING TILDE OVERLAY, COMBINING RETROFLEX HOOK BELOW, COMBINING DOUBLE MACRON BELOW, COMBINING RIGHT ARROWHEAD AND UP ARROWHEAD BELOW, COMBINING DOUBLE LOW LINE, COMBINING DOT BELOW, COMBINING RIGHT TACK BELOW, COMBINING RIGHT ARROWHEAD BELOW\n",
"c̰̟̫̲͇̺̹͖̼̦̾ͮ̍̐ͤͪ̓ͤ̐̈́̅ͯͤ̚̚͘ (multiple) LATIN SMALL LETTER C, COMBINING VERTICAL TILDE, COMBINING LATIN SMALL LETTER V, COMBINING VERTICAL LINE ABOVE, COMBINING CANDRABINDU, COMBINING LATIN SMALL LETTER E, COMBINING LEFT ANGLE ABOVE, COMBINING LATIN SMALL LETTER H, COMBINING COMMA ABOVE, COMBINING LATIN SMALL LETTER E, COMBINING LEFT ANGLE ABOVE, COMBINING CANDRABINDU, COMBINING GREEK DIALYTIKA TONOS, COMBINING OVERLINE, COMBINING LATIN SMALL LETTER X, COMBINING LATIN SMALL LETTER E, COMBINING DOT ABOVE RIGHT, COMBINING TILDE BELOW, COMBINING PLUS SIGN BELOW, COMBINING INVERTED DOUBLE ARCH BELOW, COMBINING LOW LINE, COMBINING EQUALS SIGN BELOW, COMBINING INVERTED BRIDGE BELOW, COMBINING RIGHT HALF RING BELOW, COMBINING RIGHT ARROWHEAD AND UP ARROWHEAD BELOW, COMBINING SEAGULL BELOW, COMBINING COMMA BELOW\n",
"o̴ͣ̑̐ͫ̈̄͊ͥ̓͟͏̫͔̠̤̜̤̥͘ (multiple) LATIN SMALL LETTER O, COMBINING LATIN SMALL LETTER A, COMBINING INVERTED BREVE, COMBINING CANDRABINDU, COMBINING LATIN SMALL LETTER M, COMBINING DIAERESIS, COMBINING MACRON, COMBINING NOT TILDE ABOVE, COMBINING LATIN SMALL LETTER I, COMBINING GREEK KORONIS, COMBINING DOUBLE MACRON BELOW, COMBINING TILDE OVERLAY, COMBINING GRAPHEME JOINER, COMBINING DOT ABOVE RIGHT, COMBINING INVERTED DOUBLE ARCH BELOW, COMBINING LEFT ARROWHEAD BELOW, COMBINING MINUS SIGN BELOW, COMBINING DIAERESIS BELOW, COMBINING LEFT HALF RING BELOW, COMBINING DIAERESIS BELOW, COMBINING RING BELOW\n",
"ḍ̛̥͖͓̪͈̹̯͖̱̘͙͖ͧ̿ͧ̓̓͊̈͑͘̕ (multiple) LATIN SMALL LETTER D, COMBINING LATIN SMALL LETTER U, COMBINING DOUBLE OVERLINE, COMBINING LATIN SMALL LETTER U, COMBINING COMMA ABOVE, COMBINING COMMA ABOVE, COMBINING NOT TILDE ABOVE, COMBINING DIAERESIS, COMBINING LEFT HALF RING ABOVE, COMBINING DOT ABOVE RIGHT, COMBINING COMMA ABOVE RIGHT, COMBINING HORN, COMBINING DOT BELOW, COMBINING RING BELOW, COMBINING RIGHT ARROWHEAD AND UP ARROWHEAD BELOW, COMBINING X BELOW, COMBINING BRIDGE BELOW, COMBINING DOUBLE VERTICAL LINE BELOW, COMBINING RIGHT HALF RING BELOW, COMBINING INVERTED BREVE BELOW, COMBINING RIGHT ARROWHEAD AND UP ARROWHEAD BELOW, COMBINING MACRON BELOW, COMBINING LEFT TACK BELOW, COMBINING ASTERISK BELOW, COMBINING RIGHT ARROWHEAD AND UP ARROWHEAD BELOW\n",
"e̛̺͈̜̰̜̖͎͚͈͋̒̆̈́̏͊ͬ̎̑̇̾̆̓ͬ̔̐̾ͭ́͞ (multiple) LATIN SMALL LETTER E, COMBINING HOMOTHETIC ABOVE, COMBINING TURNED COMMA ABOVE, COMBINING BREVE, COMBINING GREEK DIALYTIKA TONOS, COMBINING DOUBLE GRAVE ACCENT, COMBINING NOT TILDE ABOVE, COMBINING LATIN SMALL LETTER R, COMBINING DOUBLE VERTICAL LINE ABOVE, COMBINING INVERTED BREVE, COMBINING DOT ABOVE, COMBINING VERTICAL TILDE, COMBINING BREVE, COMBINING GREEK KORONIS, COMBINING LATIN SMALL LETTER R, COMBINING REVERSED COMMA ABOVE, COMBINING CANDRABINDU, COMBINING VERTICAL TILDE, COMBINING LATIN SMALL LETTER T, COMBINING ACUTE TONE MARK, COMBINING HORN, COMBINING DOUBLE MACRON, COMBINING INVERTED BRIDGE BELOW, COMBINING DOUBLE VERTICAL LINE BELOW, COMBINING LEFT HALF RING BELOW, COMBINING TILDE BELOW, COMBINING LEFT HALF RING BELOW, COMBINING GRAVE ACCENT BELOW, COMBINING UPWARDS ARROW BELOW, COMBINING DOUBLE RING BELOW, COMBINING DOUBLE VERTICAL LINE BELOW\n"
]
}
],
"source": [
"list_grapheme_clusters('u̶̜͓̬̞͚͙̪̰͓̯̲̝̬͔͎̳̼͇̓͊ͤ̋̃̀̄̓̿͊̀̚͟͜͟ͅņ̷͔̤̜̗̘̠̦̦̖̟͉̹͕̬͎̙̲̲̎̅̈́ͮͣ̔̀̌͂̄͆͑̚i̴̢͖̳̣̙͕̍ͯͧ̀ͥͭ̆ͣ̉͐͆̊͋͛̈́͒͟c̰̟̫̲͇̺̹͖̼̦̾ͮ̍̐ͤͪ̓ͤ̐̈́̅ͯͤ̚̚͘o̴ͣ̑̐ͫ̈̄͊ͥ̓͟͏̫͔̠̤̜̤̥͘ḍ̛̥͖͓̪͈̹̯͖̱̘͙͖ͧ̿ͧ̓̓͊̈͑͘̕e̛̺͈̜̰̜̖͎͚͈͋̒̆̈́̏͊ͬ̎̑̇̾̆̓ͬ̔̐̾ͭ́͞')"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Z LATIN CAPITAL LETTER Z\n",
"e LATIN SMALL LETTER E\n",
"u LATIN SMALL LETTER U\n",
"g LATIN SMALL LETTER G\n",
"n LATIN SMALL LETTER N\n",
"uͤ (multiple) LATIN SMALL LETTER U, COMBINING LATIN SMALL LETTER E\n",
"ß LATIN SMALL LETTER SHARP S\n"
]
}
],
"source": [
"list_grapheme_clusters('Zeugnuͤß')"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Z LATIN CAPITAL LETTER Z\n",
"e LATIN SMALL LETTER E\n",
"u LATIN SMALL LETTER U\n",
"g LATIN SMALL LETTER G\n",
"n LATIN SMALL LETTER N\n",
" private use character 0xE72B\n",
"ß LATIN SMALL LETTER SHARP S\n"
]
}
],
"source": [
"list_grapheme_clusters('Zeugnß')"
]
}
],
"metadata": {
"hide_input": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": true
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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from __future__ import division, print_function
from warnings import warn
from lxml import etree as ET
import sys
from lxml.etree import XMLSyntaxError
def alto_namespace(tree):
"""Return the ALTO namespace used in the given ElementTree.
This relies on the assumption that, in any given ALTO file, the root element has the local name "alto". We do not
check if the files uses any valid ALTO namespace.
"""
root_name = ET.QName(tree.getroot().tag)
if root_name.localname == 'alto':
return root_name.namespace
else:
raise ValueError('Not an ALTO tree')
def alto_text(tree):
"""Extract text from the given ALTO ElementTree."""
nsmap = {'alto': alto_namespace(tree)}
lines = (
' '.join(string.attrib.get('CONTENT') for string in line.iterfind('alto:String', namespaces=nsmap))
for line in tree.iterfind('.//alto:TextLine', namespaces=nsmap))
text_ = '\n'.join(lines)
return text_
def page_namespace(tree):
"""Return the PAGE content namespace used in the given ElementTree.
This relies on the assumption that, in any given PAGE content file, the root element has the local name "PcGts". We
do not check if the files uses any valid PAGE namespace.
"""
root_name = ET.QName(tree.getroot().tag)
if root_name.localname == 'PcGts':
return root_name.namespace
else:
raise ValueError('Not a PAGE tree')
def page_text(tree):
"""Extract text from the given PAGE content ElementTree."""
nsmap = {'page': page_namespace(tree)}
def region_text(region):
try:
return region.find('./page:TextEquiv/page:Unicode', namespaces=nsmap).text
except AttributeError:
return None
region_texts = []
reading_order = tree.find('.//page:ReadingOrder', namespaces=nsmap)
if reading_order is not None:
for group in reading_order.iterfind('./*', namespaces=nsmap):
if ET.QName(group.tag).localname == 'OrderedGroup':
region_ref_indexeds = group.findall('./page:RegionRefIndexed', namespaces=nsmap)
for region_ref_indexed in sorted(region_ref_indexeds, key=lambda r: int(r.attrib['index'])):
region_id = region_ref_indexed.attrib['regionRef']
region = tree.find('.//page:TextRegion[@id="%s"]' % region_id, namespaces=nsmap)
if region is not None:
region_texts.append(region_text(region))
else:
warn('Not a TextRegion: "%s"' % region_id)
else:
raise NotImplementedError
else:
for region in tree.iterfind('.//page:TextRegion', namespaces=nsmap):
region_texts.append(region_text(region))
# XXX Does a file have to have regions etc.? region vs lines etc.
# Filter empty region texts
region_texts = (t for t in region_texts if t)
text_ = '\n'.join(region_texts)
return text_
def text(filename):
"""Read the text from the given file.
Supports PAGE, ALTO and falls back to plain text.
"""
try:
tree = ET.parse(filename)
except XMLSyntaxError:
with open(filename, 'r') as f:
return f.read()
try:
return page_text(tree)
except ValueError:
return alto_text(tree)
if __name__ == '__main__':
print(text(sys.argv[1]))

22
qurator/dinglehopper/ocrd-tool.json Normal file
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{
"git_url": "https://github.com/qurator-spk/dinglehopper",
"tools": {
"ocrd-dinglehopper": {
"executable": "ocrd-dinglehopper",
"description": "Evaluate OCR text against ground truth with dinglehopper",
"input_file_grp": [
"OCR-D-GT-PAGE",
"OCR-D-OCR"
],
"output_file_grp": [
"OCR-D-OCR-EVAL"
],
"categories": [
"Quality assurance"
],
"steps": [
"recognition/text-recognition"
]
}
}
}

71
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import json
import os
import click
from ocrd import Processor
from ocrd.decorators import ocrd_cli_options, ocrd_cli_wrap_processor
from ocrd_utils import concat_padded, getLogger
from pkg_resources import resource_string
from qurator.dinglehopper.cli import process as cli_process
from qurator.dinglehopper.edit_distance import levenshtein_matrix_cache_clear
log = getLogger('processor.OcrdDinglehopperEvaluate')
OCRD_TOOL = json.loads(resource_string(__name__, 'ocrd-tool.json').decode('utf8'))
@click.command()
@ocrd_cli_options
def ocrd_dinglehopper(*args, **kwargs):
return ocrd_cli_wrap_processor(OcrdDinglehopperEvaluate, *args, **kwargs)
class OcrdDinglehopperEvaluate(Processor):
def __init__(self, *args, **kwargs):
kwargs['ocrd_tool'] = OCRD_TOOL['tools']['ocrd-dinglehopper']
super(OcrdDinglehopperEvaluate, self).__init__(*args, **kwargs)
def _make_file_id(self, input_file, input_file_grp, n):
file_id = input_file.ID.replace(input_file_grp, self.output_file_grp)
if file_id == input_file.ID:
file_id = concat_padded(self.output_file_grp, n)
return file_id
def process(self):
gt_grp, ocr_grp = self.input_file_grp.split(',')
for n, page_id in enumerate(self.workspace.mets.physical_pages):
gt_file = self.workspace.mets.find_files(fileGrp=gt_grp, pageId=page_id)[0]
ocr_file = self.workspace.mets.find_files(fileGrp=ocr_grp, pageId=page_id)[0]
log.info("INPUT FILES %i / %s↔ %s", n, gt_file, ocr_file)
file_id = self._make_file_id(ocr_file, ocr_grp, n)
report_prefix = os.path.join(self.output_file_grp, file_id)
# Process the files
try:
os.mkdir(self.output_file_grp)
except FileExistsError:
pass
cli_process(gt_file.local_filename, ocr_file.local_filename, report_prefix)
# Add reports to the workspace
for report_suffix, mimetype in \
[
['.html', 'text/html'],
['.json', 'application/json']
]:
self.workspace.add_file(
ID=file_id + report_suffix,
file_grp=self.output_file_grp,
pageId=page_id,
mimetype=mimetype,
local_filename=report_prefix + report_suffix)
# Clear cache between files
levenshtein_matrix_cache_clear()
if __name__ == '__main__':
ocrd_dinglehopper()

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import unicodedata
def substitute_equivalences(s):
# These are for OCR-D GT vs Tesseract frk vs Calamari GT4HistOCR
# It might make sense to use different rules for GT and for the different OCR
equivalences = {
'': 'ü',
'': 'ſſ',
"\ueba7": 'ſſi', # MUFI: LATIN SMALL LIGATURE LONG S LONG S I
'': 'ä',
'': 'ch',
'==': '–', # → en-dash
'—': '–', # em-dash → en-dash
'': 'ck',
'': 'll',
'': 'ö',
'': 'ſi',
'': 'ſt',
'fi': 'fi',
'ff': 'ff',
'fl': 'fl',
'ffi': 'ffi',
'': 'ct',
'’': '\'',
'⸗': '-',
'': 'tz', # MUFI: LATIN SMALL LIGATURE TZ
'aͤ': 'ä', # LATIN SMALL LETTER A, COMBINING LATIN SMALL LETTER E
'oͤ': 'ö', # LATIN SMALL LETTER O, COMBINING LATIN SMALL LETTER E
'uͤ': 'ü', # LATIN SMALL LETTER U, COMBINING LATIN SMALL LETTER E
'\uf532': 'as', # eMOP: Latin small ligature as
'\uf533': 'is', # eMOP: Latin small ligature is
'\uf534': 'us', # eMOP: Latin small ligature us
'\uf535': 'Qu', # eMOP: Latin ligature capital Q small u
'ij': 'ij', # U+0133 LATIN SMALL LIGATURE IJ
'\uE8BF': 'q&', # MUFI: LATIN SMALL LETTER Q LIGATED WITH FINAL ET XXX How to replace this correctly?
'\uEBA5': 'ſp', # MUFI: LATIN SMALL LIGATURE LONG S P
'st': 'st', # U+FB06 LATIN SMALL LIGATURE ST
'\uF50E': 'q́' # U+F50E LATIN SMALL LETTER Q WITH ACUTE ACCENT
}
s = unicodedata.normalize('NFC', s)
for fr, to in equivalences.items():
s = s.replace(fr, to)
return s

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<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
<link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/css/bootstrap.min.css" integrity="sha384-ggOyR0iXCbMQv3Xipma34MD+dH/1fQ784/j6cY/iJTQUOhcWr7x9JvoRxT2MZw1T" crossorigin="anonymous">
<style type="text/css">
.gt .diff {
color: green;
}
.ocr .diff {
color: red;
}
.ellipsis {
opacity: 0.5;
font-style: italic;
}
.diff-highlight {
border: 2px solid;
border-radius: 5px;
}
</style>
</head>
<body>
<div class="container">
{{ gt }}<br>
{{ ocr }}
<h2>Metrics</h2>
<p>CER: {{ cer|round(4) }}</p>
<p>WER: {{ wer|round(4) }}</p>
<h2>Character differences</h2>
{{ char_diff_report }}
<h2>Word differences</h2>
{{ word_diff_report }}
</div>
<script src="https://code.jquery.com/jquery-3.3.1.slim.min.js" integrity="sha384-q8i/X+965DzO0rT7abK41JStQIAqVgRVzpbzo5smXKp4YfRvH+8abtTE1Pi6jizo" crossorigin="anonymous"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/popper.js/1.14.7/umd/popper.min.js" integrity="sha384-UO2eT0CpHqdSJQ6hJty5KVphtPhzWj9WO1clHTMGa3JDZwrnQq4sF86dIHNDz0W1" crossorigin="anonymous"></script>
<script src="https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/js/bootstrap.min.js" integrity="sha384-JjSmVgyd0p3pXB1rRibZUAYoIIy6OrQ6VrjIEaFf/nJGzIxFDsf4x0xIM+B07jRM" crossorigin="anonymous"></script>
<script>
{% include 'report.html.js' %}
</script>
</body>
</html>

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function find_diff_class(classes) {
return classes.split(/\s+/).find(x => x.match(/.diff\d.*/));
}
$(document).ready(function() {
$('.diff').mouseover(function() {
let c = find_diff_class($(this).attr('class'))
$('.' + c).addClass('diff-highlight')
});
$('.diff').mouseout(function() {
let c = find_diff_class($(this).attr('class'))
$('.' + c).removeClass('diff-highlight')
});
});

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qurator/dinglehopper/templates/report.json.j2 Normal file
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{
"gt": "{{ gt }}",
"ocr": "{{ ocr }}",
"cer": {{ cer|json_float }},
"wer": {{ wer|json_float }}
}

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<?xml version="1.0" encoding="UTF-8"?>
<PcGts xmlns="http://schema.primaresearch.org/PAGE/gts/pagecontent/2019-07-15">
<Metadata>
<Creator>OCR-D/core 1.0.0b11</Creator>
<Created>2019-08-01T15:03:17.741679</Created>
<LastChange>2019-08-01T15:03:17.741679</LastChange>
<MetadataItem type="processingStep" name="recognition/text-recognition" value="ocrd-tesserocr-recognize">
<Labels>
<Label value="frk" type="model"/>
<Label value="line" type="textequiv_level"/>
<Label value="False" type="overwrite_words"/>
</Labels>
</MetadataItem>
</Metadata>
<Page imageFilename="../OCR-D-IMG-BIN/OCR-D-IMG-BIN_0002" imageWidth="1386" imageHeight="2372">
<ReadingOrder>
<OrderedGroup id="reading-order">
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<Unicode></Unicode>
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<Unicode>Die ſcheinen uns bald kleine Hügel - bald Hütten x Zelten und bald</Unicode>
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<TextLine id="region0001_line0001">
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<Unicode>„Bellen</Unicode>
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<Unicode>Den Blicken , welche ſie durchlaufen , von weiten öfters vorzuſtellen,</Unicode>
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<TextLine id="region0001_line0003">
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<Unicode>Sieht man ein ſolch gemähtes Feld - von oben,</Unicode>
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<Unicode>Sy gleicht es einem weiten Meer - worauf erhabne Wellen kobeny</Unicode>
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<Unicode>Jedoch mit dieſem Unterſcheid - daß, da ſich die beſtändig rühren:</Unicode>
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<Unicode>Die ſcheinen uns bald kleine Hügel - bald Hütten x Zelten und bald
„Bellen
Den Blicken , welche ſie durchlaufen , von weiten öfters vorzuſtellen,
Sieht man ein ſolch gemähtes Feld - von oben,
Sy gleicht es einem weiten Meer - worauf erhabne Wellen kobeny
Jedoch mit dieſem Unterſcheid - daß, da ſich die beſtändig rühren:
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<Unicode></Unicode>
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<TextLine id="region0003_line0000">
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<Unicode>Was erhebt des Schöpfers Güte</Unicode>
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<TextEquiv conf="0.8">
<Unicode>Mehr , als dieſes Seegens Meer?</Unicode>
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<TextLine id="region0003_line0002">
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<Unicode>Kommt dies wohl von ungefehv?</Unicode>
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<TextEquiv conf="0.84">
<Unicode>Nein , rüſt mein erfreut Gemühte</Unicode>
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<TextEquiv conf="0.86">
<Unicode>Nur von GOTT komint alles hers</Unicode>
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<Unicode>Ihm ſey Preiß und Dan und Ehr!</Unicode>
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<TextEquiv>
<Unicode>Was erhebt des Schöpfers Güte
Mehr , als dieſes Seegens Meer?
Kommt dies wohl von ungefehv?
Nein , rüſt mein erfreut Gemühte
Nur von GOTT komint alles hers
Ihm ſey Preiß und Dan und Ehr!</Unicode>
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<Unicode>Da Capo,</Unicode>
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<TextEquiv>
<Unicode>Da Capo,</Unicode>
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<Unicode>Geht man auf einen ſolhen Felde, ſo eben erſi gemäht - ſpaßtiereny</Unicode>
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<Coords points="89,1236 1182,1236 1182,1289 89,1289"/>
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<Unicode>Das man gewohnt voll Korn zu ſehn; ſo kommen wir uns gröſſer für,</Unicode>
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<Coords points="89,1294 1208,1294 1208,1346 89,1346"/>
<TextEquiv conf="0.85">
<Unicode>Das Feld hingegen niedriger. Auch nimmt ſodean ein neuer Scheinz</Unicode>
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<TextLine id="region0005_line0003">
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<Unicode>Und eine neue Farben Zier</Unicode>
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<Unicode>Der Grund iſt grün - die Stoppeln gelb und wenn fich unjrer Son-</Unicode>
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<TextEquiv conf="0.46">
<Unicode>nen B;Of</Unicode>
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<TextLine id="region0005_line0007">
<Coords points="68,1562 1177,1562 1177,1617 68,1617"/>
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<Unicode>Un ihre runde glatte Röhren , zumahlen früh und Abends bricht;</Unicode>
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<TextLine id="region0005_line0008">
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<TextEquiv conf="0.79">
<Unicode>So kann ein Gold kaum ſtärcker glänßen.- Dies macht ein liebliches</Unicode>
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<TextLine id="region0005_line0009">
<Coords points="777,1671 1159,1671 1159,1716 777,1716"/>
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<Unicode>Gemiſche, |</Unicode>
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<Unicode>Zutnahl wenn , in der Nachbarſchaft - ein dumfel-grünendes Gebüſche</Unicode>
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<TextLine id="region0005_line0011">
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<Unicode>Den gelben Schimmer noch erhöht. Wir ich nun jüngſt, zur Abend Zeif,</Unicode>
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<TextLine id="region0005_line0012">
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<Unicode>Durch ſo viel ſhwere Scegens-Berge, mit ſanften Schritten, hin und</Unicode>
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</TextLine>
<TextLine id="region0005_line0013">
<Coords points="800,1896 914,1896 914,1936 800,1936"/>
<TextEquiv conf="0.52">
<Unicode>Wieder;</Unicode>
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<TextLine id="region0005_line0014">
<Coords points="92,1943 1212,1943 1212,2001 92,2001"/>
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<Unicode>Gepühret durch des Feldes Schmu, gerühret durc&lt; die Fruchtbarkeitz</Unicode>
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<TextLine id="region0005_line0015">
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<Unicode>Vergmigt auf meinem Acker gieng - ertönten dieſe meine Lieder:</Unicode>
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</TextLine>
<TextEquiv>
<Unicode>Geht man auf einen ſolhen Felde, ſo eben erſi gemäht - ſpaßtiereny
Das man gewohnt voll Korn zu ſehn; ſo kommen wir uns gröſſer für,
Das Feld hingegen niedriger. Auch nimmt ſodean ein neuer Scheinz
Und eine neue Farben Zier
Den erſt gemähten Aker ein,
Der Grund iſt grün - die Stoppeln gelb und wenn fich unjrer Son-
nen B;Of
Un ihre runde glatte Röhren , zumahlen früh und Abends bricht;
So kann ein Gold kaum ſtärcker glänßen.- Dies macht ein liebliches
Gemiſche, |
Zutnahl wenn , in der Nachbarſchaft - ein dumfel-grünendes Gebüſche
Den gelben Schimmer noch erhöht. Wir ich nun jüngſt, zur Abend Zeif,
Durch ſo viel ſhwere Scegens-Berge, mit ſanften Schritten, hin und
Wieder;
Gepühret durch des Feldes Schmu, gerühret durc&lt; die Fruchtbarkeitz
Vergmigt auf meinem Acker gieng - ertönten dieſe meine Lieder:</Unicode>
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<Coords points="688,2060 1216,2060 1216,2120 688,2120"/>
<TextLine id="region0006_line0000">
<Coords points="688,2069 787,2069 787,2120 688,2120"/>
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<Unicode>5) 2</Unicode>
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<TextLine id="region0006_line0001">
<Coords points="1044,2060 1216,2060 1216,2105 1044,2105"/>
<TextEquiv conf="0.89">
<Unicode>ARIA.</Unicode>
</TextEquiv>
</TextLine>
<TextEquiv>
<Unicode>5) 2
ARIA.</Unicode>
</TextEquiv>
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</Page>
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<PcGts xmlns="http://schema.primaresearch.org/PAGE/gts/pagecontent/2018-07-15" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://schema.primaresearch.org/PAGE/gts/pagecontent/2018-07-15 http://schema.primaresearch.org/PAGE/gts/pagecontent/2018-07-15/pagecontent.xsd">
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<Creator></Creator>
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<Unicode>Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt
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dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit
amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor
invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et
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<alto xmlns="http://www.loc.gov/standards/alto/ns-v3#" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.loc.gov/standards/alto/ns-v3# http://www.loc.gov/alto/v3/alto-3-0.xsd">
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<pc:Unicode>phariſei hypocritæ, qui comeditis domos uiduarã ſub</pc:Unicode>
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<pc:Unicode>prætextu longarum precationum, propterea maiorẽ</pc:Unicode>
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<pc:Unicode>accipieris condemnationem. Ideo enim ꝙ non oratis</pc:Unicode>
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<pc:Unicode>ſecundum præſeriptum ſacræ ſcripturæ, nec ex ſpiritu</pc:Unicode>
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<pc:Unicode>&amp; ueritate ſed iuxta ueſtram propriam conſtitutionẽ,</pc:Unicode>
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<pc:Unicode>orationes ueſtræ nõ ſiunt Deo acceptæ, neq; ab eo ex⸗</pc:Unicode>
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<pc:Unicode>audiunt᷑ Eſa, Cum multiplicaueritis orationes ueſtras</pc:Unicode>
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<pc:Unicode>non exaudiam uos. Chriſtiani uero quia orant iuxta</pc:Unicode>
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<pc:Unicode>tenorem ſcripturæ, &amp; ex ſpiritu &amp; ueritate, ideo eo⸗</pc:Unicode>
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<pc:Unicode>rum orationes a Deo exaudiuntur, ſuntq; illi grat iſsi⸗</pc:Unicode>
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<pc:Unicode>mæ, dicunt enim Pater noſter qui es iu cœlis &amp;c. Vos</pc:Unicode>
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<pc:Unicode>autem hoc tenore orandi contempto, obmur muratis</pc:Unicode>
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<pc:Unicode>ueſtras Horas canonicas, hoc eſt, diabolicas ab Anti⸗</pc:Unicode>
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<pc:Unicode>chriſto inſtitutas. Paulus mauult quinq; uerba in Ec⸗</pc:Unicode>
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<pc:Unicode>cle ſia loqui in ſenſu, qß decem milia uerborum in lin⸗</pc:Unicode>
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<pc:Unicode>ua, Quibus uerbis adeo dãnat ueſtras prolixas ora⸗</pc:Unicode>
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<pc:Unicode>tiones, ut ſi ſemiuncia ſanæ mentis uel mica ſidei eſfet</pc:Unicode>
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<pc:Unicode>in uobis, eas ſine dubio omitteretis.</pc:Unicode>
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<pc:Unicode>De inuocatione diuorum ne apiculus quidem ha</pc:Unicode>
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<pc:Unicode>betur in ſacris literis, quare ter ſtulti eſtis quod inuo⸗</pc:Unicode>
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<pc:Unicode>catis ſanctos, cum ex præce pto Dei ne mo inuocandus</pc:Unicode>
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<pc:Unicode>ſit niſi ſolus Deus. Inuoca inquit me in die tribulatio⸗</pc:Unicode>
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<pc:Unicode>nis. &amp; eruam te, &amp; honorificabis me. Et omnis qui⸗</pc:Unicode>
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<pc:Unicode>cumq; inuocauerit nomen domini, ſaluus erit Sed</pc:Unicode>
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<pc:Unicode>quomodo inuocabitis, in quem non credidiſtis? Quo</pc:Unicode>
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<pc:Unicode>modo credetis ſine uerbo ? Inuocationẽ ergo in ſcrip⸗</pc:Unicode>
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<pc:Unicode>turis non legitis cõmemorationem uero ſæpe, non ut</pc:Unicode>
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<pc:Unicode>intercedant pro uobis ſancti, ſed nt meminerit Deus</pc:Unicode>
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<pc:Unicode>Teſtamenti cum patribus ſanctis pacti, ut ſimiliter uo⸗</pc:Unicode>
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<pc:Unicode>biſcum agat per miſericordiam, quemadmodum cum</pc:Unicode>
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<pc:Unicode>ilis egit. Atq; hoc non eſt inuocare ſanctos. ſed Deum</pc:Unicode>
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<pc:Unicode>ſuæ miſericordiæ &amp; promiſsionis admonere Sic pſal</pc:Unicode>
</pc:TextEquiv>
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<pc:Unicode>mographus dicit, Qui paſcis Iſrael attende, qui de⸗</pc:Unicode>
</pc:TextEquiv>
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<pc:Unicode>ducis uelut ouem Iacob Sic &amp; Moſes orat, Memento</pc:Unicode>
</pc:TextEquiv>
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<pc:Unicode>B 3 domi⸗</pc:Unicode>
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<pc:TextEquiv>
<pc:Unicode>phariſei hypocritæ, qui comeditis domos uiduarã ſub
prætextu longarum precationum, propterea maiorẽ
accipieris condemnationem. Ideo enim ꝙ non oratis
ſecundum præſeriptum ſacræ ſcripturæ, nec ex ſpiritu
&amp; ueritate ſed iuxta ueſtram propriam conſtitutionẽ,
orationes ueſtræ nõ ſiunt Deo acceptæ, neq; ab eo ex⸗
audiunt᷑ Eſa, Cum multiplicaueritis orationes ueſtras
non exaudiam uos. Chriſtiani uero quia orant iuxta
tenorem ſcripturæ, &amp; ex ſpiritu &amp; ueritate, ideo eo⸗
rum orationes a Deo exaudiuntur, ſuntq; illi grat iſsi⸗
mæ, dicunt enim Pater noſter qui es iu cœlis &amp;c. Vos
autem hoc tenore orandi contempto, obmur muratis
ueſtras Horas canonicas, hoc eſt, diabolicas ab Anti⸗
chriſto inſtitutas. Paulus mauult quinq; uerba in Ec⸗
cle ſia loqui in ſenſu, qß decem milia uerborum in lin⸗
ua, Quibus uerbis adeo dãnat ueſtras prolixas ora⸗
tiones, ut ſi ſemiuncia ſanæ mentis uel mica ſidei eſfet
in uobis, eas ſine dubio omitteretis.
De inuocatione diuorum ne apiculus quidem ha
betur in ſacris literis, quare ter ſtulti eſtis quod inuo⸗
catis ſanctos, cum ex præce pto Dei ne mo inuocandus
ſit niſi ſolus Deus. Inuoca inquit me in die tribulatio⸗
nis. &amp; eruam te, &amp; honorificabis me. Et omnis qui⸗
cumq; inuocauerit nomen domini, ſaluus erit Sed
quomodo inuocabitis, in quem non credidiſtis? Quo
modo credetis ſine uerbo ? Inuocationẽ ergo in ſcrip⸗
turis non legitis cõmemorationem uero ſæpe, non ut
intercedant pro uobis ſancti, ſed nt meminerit Deus
Teſtamenti cum patribus ſanctis pacti, ut ſimiliter uo⸗
biſcum agat per miſericordiam, quemadmodum cum
ilis egit. Atq; hoc non eſt inuocare ſanctos. ſed Deum
ſuæ miſericordiæ &amp; promiſsionis admonere Sic pſal
mographus dicit, Qui paſcis Iſrael attende, qui de⸗
ducis uelut ouem Iacob Sic &amp; Moſes orat, Memento
B 3 domi⸗</pc:Unicode>
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<TextLine HEIGHT="49" WIDTH="1227" VPOS="2023" HPOS="361"><String WC="0.6323529482" CONTENT="zusammengewachsen" HEIGHT="43" WIDTH="350" VPOS="2028" HPOS="361"/><SP WIDTH="32" VPOS="2038" HPOS="711"/><String WC="0.7599999905" CONTENT="erscheinen" HEIGHT="44" WIDTH="163" VPOS="2027" HPOS="743"/><SP WIDTH="26" VPOS="2025" HPOS="906"/><String WC="0.8854545355" CONTENT="(Cosmarium," HEIGHT="44" WIDTH="238" VPOS="2025" HPOS="932"/><SP WIDTH="31" VPOS="2026" HPOS="1170"/><String WC="0.7774999738" CONTENT="Fig." HEIGHT="42" WIDTH="68" VPOS="2026" HPOS="1201"/><SP WIDTH="30" VPOS="2029" HPOS="1269"/><String WC="0.6140000224" CONTENT="134)," HEIGHT="43" WIDTH="84" VPOS="2024" HPOS="1299"/><SP WIDTH="34" VPOS="2035" HPOS="1383"/><String WC="0.6825000048" CONTENT="oder" HEIGHT="33" WIDTH="72" VPOS="2025" HPOS="1417"/><SP WIDTH="24" VPOS="2034" HPOS="1489"/><String WC="0.6833333373" CONTENT="man" HEIGHT="24" WIDTH="75" VPOS="2034" HPOS="1513"/></TextLine>
<TextLine HEIGHT="47" WIDTH="1223" VPOS="2079" HPOS="365"><String WC="0.7799999714" CONTENT="findet" HEIGHT="41" WIDTH="94" VPOS="2083" HPOS="365"/><SP WIDTH="18" VPOS="2083" HPOS="460"/><String WC="0.8355555534" CONTENT="türkische" HEIGHT="44" WIDTH="142" VPOS="2082" HPOS="479"/><SP WIDTH="15" VPOS="2083" HPOS="622"/><String WC="0.6140000224" CONTENT="Halbmonde," HEIGHT="43" WIDTH="203" VPOS="2082" HPOS="638"/><SP WIDTH="20" VPOS="2083" HPOS="842"/><String WC="0.7233333588" CONTENT="die" HEIGHT="34" WIDTH="46" VPOS="2082" HPOS="863"/><SP WIDTH="21" VPOS="2092" HPOS="910"/><String WC="0.5899999738" CONTENT="genau" HEIGHT="33" WIDTH="101" VPOS="2091" HPOS="932"/><SP WIDTH="17" VPOS="2081" HPOS="1034"/><String WC="0.6620000005" CONTENT="durch" HEIGHT="43" WIDTH="86" VPOS="2081" HPOS="1052"/><SP WIDTH="20" VPOS="2081" HPOS="1139"/><String WC="0.6340000033" CONTENT="einen" HEIGHT="35" WIDTH="87" VPOS="2080" HPOS="1160"/><SP WIDTH="15" VPOS="2081" HPOS="1248"/><String WC="0.7910000086" CONTENT="Ouerstrich" HEIGHT="43" WIDTH="168" VPOS="2080" HPOS="1264"/><SP WIDTH="21" VPOS="2080" HPOS="1433"/><String WC="0.5950000286" CONTENT="halbiert" HEIGHT="44" WIDTH="133" VPOS="2079" HPOS="1455"/></TextLine>
<TextLine HEIGHT="50" WIDTH="1222" VPOS="2133" HPOS="365"><String WC="0.5674999952" CONTENT="sind" HEIGHT="43" WIDTH="62" VPOS="2137" HPOS="365"/><SP WIDTH="37" VPOS="2137" HPOS="428"/><String WC="0.8000000119" CONTENT="und" HEIGHT="34" WIDTH="61" VPOS="2137" HPOS="466"/><SP WIDTH="38" VPOS="2136" HPOS="528"/><String WC="0.6499999762" CONTENT="an" HEIGHT="24" WIDTH="40" VPOS="2147" HPOS="567"/><SP WIDTH="33" VPOS="2137" HPOS="608"/><String WC="0.8183333278" CONTENT="beiden" HEIGHT="35" WIDTH="107" VPOS="2137" HPOS="642"/><SP WIDTH="34" VPOS="2138" HPOS="750"/><String WC="0.4499999881" CONTENT="Enden" HEIGHT="34" WIDTH="106" VPOS="2137" HPOS="785"/><SP WIDTH="36" VPOS="2137" HPOS="892"/><String WC="0.8600000143" CONTENT="je" HEIGHT="44" WIDTH="27" VPOS="2137" HPOS="929"/><SP WIDTH="34" VPOS="2146" HPOS="957"/><String WC="0.7225000262" CONTENT="eine" HEIGHT="34" WIDTH="64" VPOS="2136" HPOS="992"/><SP WIDTH="33" VPOS="2136" HPOS="1057"/><String WC="0.9139999747" CONTENT="kreisrunde" HEIGHT="35" WIDTH="180" VPOS="2135" HPOS="1091"/><SP WIDTH="33" VPOS="2135" HPOS="1272"/><String WC="0.6079999804" CONTENT="Blase" HEIGHT="43" WIDTH="89" VPOS="2135" HPOS="1306"/><SP WIDTH="33" VPOS="2145" HPOS="1396"/><String WC="0.7266666889" CONTENT="enthalten" HEIGHT="46" WIDTH="157" VPOS="2133" HPOS="1430"/></TextLine>
</TextBlock><GraphicalElement ID="Page1_Block8" HEIGHT="184" WIDTH="8" VPOS="900" HPOS="1258"/><GraphicalElement ID="Page1_Block9" HEIGHT="90" WIDTH="3" VPOS="896" HPOS="1427"/><GraphicalElement ID="Page1_Block10" HEIGHT="146" WIDTH="7" VPOS="885" HPOS="1544"/>
</PrintSpace>
</Page>
</Layout>
</alto>

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<?xml version="1.0" encoding="UTF-8"?>
<alto xmlns="http://www.loc.gov/standards/alto/ns-v3#">
<Layout>
<Page WIDTH="1148" HEIGHT="1852" PHYSICAL_IMG_NR="0" ID="page_0">
<PrintSpace HPOS="0" VPOS="0" WIDTH="1148" HEIGHT="1852">
<TextBlock ID="block_3" HPOS="135" VPOS="251" WIDTH="741" HEIGHT="47">
<TextLine ID="line_3" HPOS="135" VPOS="251" WIDTH="741" HEIGHT="47">
<String ID="string_5" HPOS="135" VPOS="251" WIDTH="65" HEIGHT="34" WC="0.89" CONTENT="über"/><SP WIDTH="19" VPOS="251" HPOS="200"/>
<String ID="string_6" HPOS="219" VPOS="256" WIDTH="41" HEIGHT="31" WC="0.96" CONTENT="die"/><SP WIDTH="23" VPOS="256" HPOS="260"/>
<String ID="string_7" HPOS="283" VPOS="258" WIDTH="87" HEIGHT="30" WC="0.87" CONTENT="vielen"/><SP WIDTH="16" VPOS="258" HPOS="370"/>
<String ID="string_8" HPOS="386" VPOS="259" WIDTH="118" HEIGHT="37" WC="0.96" CONTENT="Sorgen"/><SP WIDTH="14" VPOS="259" HPOS="504"/>
<String ID="string_9" HPOS="518" VPOS="265" WIDTH="90" HEIGHT="32" WC="0.21" CONTENT="wegen"/><SP WIDTH="12" VPOS="265" HPOS="608"/>
<String ID="string_10" HPOS="620" VPOS="254" WIDTH="130" HEIGHT="42" WC="0.21" CONTENT="deſſelben"/><SP WIDTH="24" VPOS="254" HPOS="750"/>
<String ID="string_11" HPOS="774" VPOS="255" WIDTH="102" HEIGHT="43" WC="0.74" CONTENT="vergaß"/>
</TextLine>
</TextBlock>
<TextBlock ID="block_4" HPOS="134" VPOS="304" WIDTH="740" HEIGHT="40">
<TextLine ID="line_4" HPOS="134" VPOS="304" WIDTH="740" HEIGHT="40">
<String ID="string_12" HPOS="134" VPOS="304" WIDTH="203" HEIGHT="40" WC="0.75" CONTENT="Hartkopf,"/><SP WIDTH="30" VPOS="304" HPOS="337"/>
<String ID="string_13" HPOS="367" VPOS="310" WIDTH="45" HEIGHT="27" WC="0.93" CONTENT="der"/><SP WIDTH="24" VPOS="310" HPOS="412"/>
<String ID="string_14" HPOS="436" VPOS="309" WIDTH="74" HEIGHT="35" WC="0.59" CONTENT="Frau"/><SP WIDTH="22" VPOS="309" HPOS="510"/>
<String ID="string_15" HPOS="532" VPOS="306" WIDTH="189" HEIGHT="36" WC="0.23" CONTENT="Amtmännin"/><SP WIDTH="16" VPOS="306" HPOS="721"/>
<String ID="string_16" HPOS="737" VPOS="307" WIDTH="66" HEIGHT="34" WC="0.52" CONTENT="das"/><SP WIDTH="16" VPOS="307" HPOS="803"/>
<String ID="string_17" HPOS="819" VPOS="318" WIDTH="55" HEIGHT="24" WC="0.0" CONTENT="ver-"/>
</TextLine>
</TextBlock>
<TextBlock ID="block_5" HPOS="134" VPOS="356" WIDTH="761" HEIGHT="46">
<TextLine ID="line_5" HPOS="134" VPOS="356" WIDTH="761" HEIGHT="46">
<String ID="string_18" HPOS="134" VPOS="356" WIDTH="137" HEIGHT="37" WC="0.92" CONTENT="ſprochene"/><SP WIDTH="31" VPOS="356" HPOS="271"/>
<String ID="string_19" HPOS="302" VPOS="365" WIDTH="32" HEIGHT="30" WC="0.73" CONTENT="zu"/><SP WIDTH="29" VPOS="365" HPOS="334"/>
<String ID="string_20" HPOS="363" VPOS="356" WIDTH="170" HEIGHT="39" WC="0.52" CONTENT="überliefern."/><SP WIDTH="28" VPOS="356" HPOS="533"/>
</TextLine>
</TextBlock>
</PrintSpace>
</Page>
</Layout>
</alto>

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qurator/dinglehopper/tests/data/test.page2018.xml Normal file
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Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.

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from .util import unzip
from .. import align, seq_align, distance
def test_left_empty():
result = list(align('', 'foo'))
expected = [(None, 'f'), (None, 'o'), (None, 'o')]
assert result == expected
def test_right_empty():
result = list(align('foo', ''))
expected = [('f', None), ('o', None), ('o', None)]
assert result == expected
def test_left_longer():
result = list(align('food', 'foo'))
expected = [('f', 'f'), ('o', 'o'), ('o', 'o'), ('d', None)]
assert result == expected
def test_right_longer():
result = list(align('foo', 'food'))
expected = [('f', 'f'), ('o', 'o'), ('o', 'o'), (None, 'd')]
assert result == expected
def test_some_diff():
result = list(align('abcde', 'aaadef'))
left, right = unzip(result)
assert list(left) == ['a', 'b', 'c', 'd', 'e', None]
assert list(right) == ['a', 'a', 'a', 'd', 'e', 'f']
def test_longer():
s1 = 'Dies ist eine Tst!'
s2 = 'Dies ist ein Test.'
result = list(align(s1, s2)) # ; diffprint(*unzip(result))
expected = [('D', 'D'), ('i', 'i'), ('e', 'e'), ('s', 's'), (' ', ' '),
('i', 'i'), ('s', 's'), ('t', 't'), (' ', ' '),
('e', 'e'), ('i', 'i'), ('n', 'n'), ('e', None), (' ', ' '),
('T', 'T'), (None, 'e'), ('s', 's'), ('t', 't'), ('!', '.')]
assert result == expected
def test_completely_different():
assert len(list(align('abcde', 'fghij'))) == 5
def test_with_some_fake_ocr_errors():
result = list(align('Über die vielen Sorgen wegen desselben vergaß',
'SomeJunk MoreJunk Übey die vielen Sorgen wegen AdditionalJunk deffelben vcrgab'))
left, right = unzip(result)
# Beginning
assert list(left[:18]) == [None]*18
assert list(right[:18]) == list('SomeJunk MoreJunk ')
# End
assert list(left[-1:]) == ['ß']
assert list(right[-1:]) == ['b']
def test_lines():
"""Test comparing list of lines.
This mainly serves as documentation for comparing lists of lines.
"""
result = list(seq_align(
['This is a line.', 'This is another', 'And the last line'],
['This is a line.', 'This is another', 'J u n k', 'And the last line']
))
left, right = unzip(result)
assert list(left) == ['This is a line.', 'This is another', None, 'And the last line']
assert list(right) == ['This is a line.', 'This is another', 'J u n k', 'And the last line']
def test_lines_similar():
"""Test comparing list of lines while using a "weaker equivalence".
This mainly serves as documentation.
"""
class SimilarString:
def __init__(self, string):
self._string = string
def __eq__(self, other):
return distance(self._string, other._string) < 2 # XXX NOT the final version
def __ne__(self, other):
return not self.__eq__(other)
def __repr__(self):
return 'SimilarString(\'%s\')' % self._string
def __hash__(self):
return hash(self._string)
result = list(seq_align(
[SimilarString('This is a line.'), SimilarString('This is another'), SimilarString('And the last line')],
[SimilarString('This is a ljne.'), SimilarString('This is another'), SimilarString('J u n k'), SimilarString('And the last line')]
))
left, right = unzip(result)
assert list(left) == [SimilarString('This is a line.'), SimilarString('This is another'), None, SimilarString('And the last line')]
assert list(right) == [SimilarString('This is a ljne.'), SimilarString('This is another'), SimilarString('J u n k'), SimilarString('And the last line')]

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from __future__ import division, print_function
import math
import unicodedata
from .. import character_error_rate
def test_character_error_rate():
assert character_error_rate('a', 'a') == 0
assert character_error_rate('a', 'b') == 1/1
assert character_error_rate('Foo', 'Bar') == 3/3
assert character_error_rate('Foo', '') == 3/3
assert character_error_rate('', '') == 0
assert math.isinf(character_error_rate('', 'Foo'))
assert character_error_rate('Foo', 'Food') == 1/3
assert character_error_rate('Fnord', 'Food') == 2/5
assert character_error_rate('Müll', 'Mull') == 1/4
assert character_error_rate('Abstand', 'Sand') == 4/7
def test_character_error_rate_hard():
s1 = unicodedata.normalize('NFC', 'Schlyñ lorem ipsum.')
s2 = unicodedata.normalize('NFD', 'Schlyñ lorem ipsum!') # Different, decomposed!
assert character_error_rate(s1, s2) == 1/19
s1 = 'Schlyñ'
assert len(s1) == 6 # This ends with LATIN SMALL LETTER N WITH TILDE, so 6 code points
s2 = 'Schlym̃'
assert len(s2) == 7 # This, OTOH, ends with LATIN SMALL LETTER M + COMBINING TILDE, 7 code points
# Both strings have the same length in terms of grapheme clusters. So the CER should be symmetrical.
assert character_error_rate(s2, s1) == 1/6
assert character_error_rate(s1, s2) == 1/6

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from __future__ import division, print_function
import unicodedata
from .. import levenshtein, distance
def test_levenshtein():
assert levenshtein('a', 'a') == 0
assert levenshtein('a', 'b') == 1
assert levenshtein('Foo', 'Bar') == 3
assert levenshtein('', '') == 0
assert levenshtein('Foo', '') == 3
assert levenshtein('', 'Foo') == 3
assert levenshtein('Foo', 'Food') == 1
assert levenshtein('Fnord', 'Food') == 2
assert levenshtein('Müll', 'Mull') == 1
assert levenshtein('Abstand', 'Sand') == 4
def test_levenshtein_other_sequences():
assert levenshtein(['a', 'ab'], ['a', 'ab', 'c']) == 1
assert levenshtein(['a', 'ab'], ['a', 'c']) == 1
def test_distance():
assert distance('Fnord', 'Food') == 2
assert distance('Müll', 'Mull') == 1
word1 = unicodedata.normalize('NFC', 'Schlyñ')
word2 = unicodedata.normalize('NFD', 'Schlyñ') # Different, decomposed!
assert distance(word1, word2) == 0
word1 = 'Schlyñ'
assert len(word1) == 6 # This ends with LATIN SMALL LETTER N WITH TILDE, so 6 code points
word2 = 'Schlym̃'
assert len(word2) == 7 # This, OTOH, ends with LATIN SMALL LETTER M + COMBINING TILDE, 7 code points
assert distance(word1, word2) == 1

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import unicodedata
from .. import seq_editops, editops
def test_trivial():
assert seq_editops('abc', 'abc') == []
assert seq_editops('', '') == []
def test_insert():
assert seq_editops('bc', 'abc') == [('insert', 0, 0)]
assert seq_editops('ac', 'abc') == [('insert', 1, 1)]
assert seq_editops('ab', 'abc') == [('insert', 2, 2)]
assert seq_editops('', 'a') == [('insert', 0, 0)]
def test_multiple():
assert seq_editops('bcd', 'abce') == [('insert', 0, 0), ('replace', 2, 3)]
def test_delete():
assert seq_editops('abcdef', 'cdef') == [('delete', 0, 0), ('delete', 1, 0)]
assert seq_editops('Xabcdef', 'Xcdef') == [('delete', 1, 1), ('delete', 2, 1)]
assert seq_editops('abcdefg', 'acdefX') == [('delete', 1, 1), ('replace', 6, 5)]
assert seq_editops('abcde', 'aabcd') == [('insert', 1, 1), ('delete', 4, 5)]
assert seq_editops('Foo', '') == [('delete', 0, 0), ('delete', 1, 0), ('delete', 2, 0)]
assert seq_editops('Foolish', 'Foo') == [('delete', 3, 3), ('delete', 4, 3), ('delete', 5, 3), ('delete', 6, 3)]
def test_ambiguous():
assert seq_editops('bcd', 'abcef') == [('insert', 0, 0), ('replace', 2, 3), ('insert', 3, 4)]
def test_editops():
"""Test editops() in cases where dealing with grapheme clusters matters"""
# In these cases, one of the words has a composed form, the other one does not.
assert editops('Schlyñ', 'Schlym̃') == [('replace', 5, 5)]
assert editops('oͤde', 'öde') == [('replace', 0, 0)]
def test_editops_canonically_equivalent():
left = unicodedata.lookup('LATIN SMALL LETTER N') + unicodedata.lookup('COMBINING TILDE')
right = unicodedata.lookup('LATIN SMALL LETTER N WITH TILDE')
assert left != right
assert unicodedata.normalize('NFC', left) == unicodedata.normalize('NFC', right)
assert editops(left, right) == []

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from __future__ import division, print_function
import os
import pytest
from lxml import etree as ET
from .. import align, page_text
data_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'data')
@pytest.mark.integration
def test_align_page_files():
# In the fake OCR file, we changed 2 characters and replaced a fi ligature with fi.
# → 4 elements in the alignment should be different.
# NOTE: In this example, it doesn't matter that we work with "characters", not grapheme clusters.
gt = page_text(ET.parse(os.path.join(data_dir, 'test-gt.page2018.xml')))
ocr = page_text(ET.parse(os.path.join(data_dir, 'test-fake-ocr.page2018.xml')))
result = list(align(gt, ocr))
assert sum(left != right for left, right in result) == 4

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qurator/dinglehopper/tests/test_integ_character_error_rate_ocr.py Normal file
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from __future__ import division, print_function
import os
import pytest
from lxml import etree as ET
from .. import character_error_rate, page_text, alto_text
data_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'data')
@pytest.mark.integration
def test_character_error_rate_between_page_files():
# In the fake OCR file, we changed 2 characters and replaced a fi ligature with fi.
gt = page_text(ET.parse(os.path.join(data_dir, 'test-gt.page2018.xml')))
ocr = page_text(ET.parse(os.path.join(data_dir, 'test-fake-ocr.page2018.xml')))
assert character_error_rate(gt, ocr) == 4/(470 + 1 + 311) # 2 TextRegions, 1 \n
@pytest.mark.integration
def test_character_error_rate_between_page_alto():
gt = page_text(ET.parse(os.path.join(data_dir, 'lorem-ipsum', 'lorem-ipsum-scan.gt.page.xml')))
ocr = alto_text(ET.parse(os.path.join(data_dir, 'lorem-ipsum', 'lorem-ipsum-scan.ocr.tesseract.alto.xml')))
assert gt == ocr
assert character_error_rate(gt, ocr) == 0
@pytest.mark.integration
def test_character_error_rate_between_page_alto_2():
gt = page_text(ET.parse(os.path.join(data_dir, 'lorem-ipsum', 'lorem-ipsum-scan-bad.gt.page.xml')))
ocr = alto_text(ET.parse(os.path.join(data_dir, 'lorem-ipsum', 'lorem-ipsum-scan-bad.ocr.tesseract.alto.xml')))
assert character_error_rate(gt, ocr) == 8/591 # Manually verified

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import os
import json
import pytest
from .util import working_directory
from ..cli import process
def test_cli_json(tmp_path):
"""Test that the cli/process() yields a loadable JSON report"""
# XXX Path.__str__() is necessary for Python 3.5
with working_directory(str(tmp_path)):
with open('gt.txt', 'w') as gtf:
gtf.write('AAAAA')
with open('ocr.txt', 'w') as ocrf:
ocrf.write('AAAAB')
process('gt.txt', 'ocr.txt', 'report')
with open('report.json', 'r') as jsonf:
j = json.load(jsonf)
assert j['cer'] == pytest.approx(0.2)
def test_cli_json_cer_is_infinity(tmp_path):
"""Test that the cli/process() yields a loadable JSON report when CER == inf"""
# XXX Path.__str__() is necessary for Python 3.5
with working_directory(str(tmp_path)):
with open('gt.txt', 'w') as gtf:
gtf.write('') # Empty to yield CER == inf
with open('ocr.txt', 'w') as ocrf:
ocrf.write('Not important')
process('gt.txt', 'ocr.txt', 'report')
with open('report.json', 'r') as jsonf:
j = json.load(jsonf)
assert j['cer'] == pytest.approx(float('inf'))

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from __future__ import division, print_function
import os
import pytest
from lxml import etree as ET
from .. import distance, page_text, alto_text
data_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'data')
@pytest.mark.integration
def test_distance_between_page_files():
# In the fake OCR file, we changed 2 characters and replaced a fi ligature with fi.
gt = page_text(ET.parse(os.path.join(data_dir, 'test-gt.page2018.xml')))
ocr = page_text(ET.parse(os.path.join(data_dir, 'test-fake-ocr.page2018.xml')))
assert distance(gt, ocr) == 4
@pytest.mark.integration
def test_distance_between_page_alto():
gt = page_text(ET.parse(os.path.join(data_dir, 'lorem-ipsum', 'lorem-ipsum-scan.gt.page.xml')))
ocr = alto_text(ET.parse(os.path.join(data_dir, 'lorem-ipsum', 'lorem-ipsum-scan.ocr.tesseract.alto.xml')))
assert gt == ocr
assert distance(gt, ocr) == 0
@pytest.mark.integration
def test_distance_between_page_alto_2():
gt = page_text(ET.parse(os.path.join(data_dir, 'lorem-ipsum', 'lorem-ipsum-scan-bad.gt.page.xml')))
ocr = alto_text(ET.parse(os.path.join(data_dir, 'lorem-ipsum', 'lorem-ipsum-scan-bad.ocr.tesseract.alto.xml')))
assert distance(gt, ocr) == 8 # Manually verified

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qurator/dinglehopper/tests/test_integ_ocrd_cli.py Normal file
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import os
import re
import shutil
import json
from pathlib import Path
from click.testing import CliRunner
import pytest
from .util import working_directory
from ..ocrd_cli import ocrd_dinglehopper
data_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'data')
def test_ocrd_cli(tmp_path):
"""Test OCR-D interface"""
# XXX Path.str() is necessary for Python 3.5
# Copy test workspace
test_workspace_dir_source = Path(data_dir) / 'actevedef_718448162'
test_workspace_dir = tmp_path / 'test_ocrd_cli'
shutil.copytree(str(test_workspace_dir_source), str(test_workspace_dir))
# Run through the OCR-D interface
with working_directory(str(test_workspace_dir)):
runner = CliRunner()
result = runner.invoke(ocrd_dinglehopper, [
'-m', 'mets.xml',
'-I', 'OCR-D-GT-PAGE,OCR-D-OCR-CALAMARI',
'-O', 'OCR-D-OCR-CALAMARI-EVAL'
])
assert result.exit_code == 0
result_json = list((test_workspace_dir / 'OCR-D-OCR-CALAMARI-EVAL').glob('*.json'))
assert json.load(open(str(result_json[0])))['cer'] < 0.03

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from __future__ import division, print_function
import os
import pytest
from lxml import etree as ET
from .. import word_error_rate, words, page_text, alto_text
data_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'data')
@pytest.mark.integration
def test_word_error_rate_between_page_files():
# In the fake OCR file, we changed 2 characters and replaced a fi ligature with fi. → 3 changed words
gt = page_text(ET.parse(os.path.join(data_dir, 'test-gt.page2018.xml')))
gt_word_count = 7+6+5+8+7+6+7+8+6+7+7+5+6+8+8+7+7+6+5+4 # Manually verified word count per line
assert len(list(words(gt))) == gt_word_count
ocr = page_text(ET.parse(os.path.join(data_dir, 'test-fake-ocr.page2018.xml')))
assert word_error_rate(gt, ocr) == 3/gt_word_count
@pytest.mark.integration
def test_word_error_rate_between_page_alto():
gt = page_text(ET.parse(os.path.join(data_dir, 'lorem-ipsum', 'lorem-ipsum-scan.gt.page.xml')))
ocr = alto_text(ET.parse(os.path.join(data_dir, 'lorem-ipsum', 'lorem-ipsum-scan.ocr.tesseract.alto.xml')))
assert gt == ocr
assert word_error_rate(gt, ocr) == 0
@pytest.mark.integration
def test_word_error_rate_between_page_alto_2():
gt = page_text(ET.parse(os.path.join(data_dir, 'lorem-ipsum', 'lorem-ipsum-scan-bad.gt.page.xml')))
gt_word_count = 14+18+17+14+17+17+3 # Manually verified word count per line
assert len(list(words(gt))) == gt_word_count
ocr = alto_text(ET.parse(os.path.join(data_dir, 'lorem-ipsum', 'lorem-ipsum-scan-bad.ocr.tesseract.alto.xml')))
assert word_error_rate(gt, ocr) == 7/gt_word_count # Manually verified, 6 words are wrong, 1 got split (=2 errors)

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import os
import re
import lxml.etree as ET
import textwrap
import pytest
from .. import alto_namespace, alto_text, page_namespace, page_text, text
data_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'data')
def test_alto_namespace():
tree = ET.parse(os.path.join(data_dir, 'test.alto3.xml'))
assert alto_namespace(tree) == 'http://www.loc.gov/standards/alto/ns-v3#'
def test_alto_text():
tree = ET.parse(os.path.join(data_dir, 'test.alto3.xml'))
result = alto_text(tree)
expected = textwrap.dedent("""\
über die vielen Sorgen wegen deſſelben vergaß
Hartkopf, der Frau Amtmännin das ver-
ſprochene zu überliefern.""")
assert result == expected
def test_alto_text_ALTO1():
tree = ET.parse(os.path.join(data_dir, 'test.alto1.xml'))
assert "being erected at the Broadway stock" in alto_text(tree)
def test_alto_text_ALTO2():
tree = ET.parse(os.path.join(data_dir, 'test.alto2.xml'))
assert "Halbmonde, die genau durch einen Ouerstrich halbiert\nsind und an beiden Enden" in alto_text(tree)
def test_alto_text_ALTO3():
tree = ET.parse(os.path.join(data_dir, 'test.alto3.xml'))
assert "über die vielen Sorgen wegen deſſelben vergaß" in alto_text(tree)
def test_page_namespace():
tree = ET.parse(os.path.join(data_dir, 'test.page2018.xml'))
assert page_namespace(tree) == 'http://schema.primaresearch.org/PAGE/gts/pagecontent/2018-07-15'
def test_page_test():
tree = ET.parse(os.path.join(data_dir, 'test.page2018.xml'))
result = page_text(tree)
expected = textwrap.dedent("""\
ber die vielen Sorgen wegen deelben vergaß
Hartkopf, der Frau Amtmnnin das ver⸗
ſproene zu berliefern. — Ein Erpreer
wurde an ihn abgeſit, um ihn ums Him⸗
melswien zu ſagen, daß er das Verſproene
glei den Augenbli berbringen mte, die
Frau Amtmnnin htte auf ihn verlaen,
und nun wßte e nit, was e anfangen
ſote. Den Augenbli ſote er kommen,
ſon vergieng e in ihrer Ang. — Die
Ge wren ſon angekommen, und es fehlte
ihr do no an aem. —
Hartkopf mußte er bennen, und
endli na langem Nadenken fiel es ihm er
wieder ein. — Er langte den Zettel aus dem
Accisbue heraus, und ſagte ſeiner Frau, daß
e das, was da wre, herbeyſaffen mte.
Jndeß mangelten do einige Generalia, die
alſo wegfielen. — Hartkopf gieng ſelb
mit und berbrate es. —""")
assert result == expected
def test_page_with_empty_region():
# This file contains an empty TextRegion:
#
# <TextRegion id="region0000">
# <Coords points="488,133 1197,133 1197,193 488,193"/>
# <TextEquiv>
# <Unicode></Unicode>
# </TextEquiv>
# </TextRegion>
tree = ET.parse(os.path.join(data_dir, 'brochrnx_73075507X/00000139.ocrd-tess.ocr.page.xml'))
result = page_text(tree)
assert result
def test_page_order():
# This file contains TextRegions where file order is not the same as reading order.
tree = ET.parse(os.path.join(data_dir, 'order.page.xml'))
result = page_text(tree)
assert re.search(r'Herr Konfrater.*75.*Etwas f.r Wittwen.*Ein gewi.er Lord.*76\. Die', result, re.DOTALL)
def test_page_mixed_regions():
# This file contains ImageRegions and TextRegions in the ReadingOrder
tree = ET.parse(os.path.join(data_dir, 'mixed-regions.page.xml'))
with pytest.warns(UserWarning, match=r'Not a TextRegion'):
result = page_text(tree)
assert 'non exaudiam uos. Chriſtiani uero quia orant iuxta' in result
def test_text():
assert "being erected at the Broadway stock" in text(os.path.join(data_dir, 'test.alto1.xml'))
assert "wieder ein. — Er langte den Zettel aus dem" in text(os.path.join(data_dir, 'test.page2018.xml'))
assert "Lorem ipsum" in text(os.path.join(data_dir, 'test.txt'))

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from __future__ import division, print_function
import math
from .. import word_error_rate, words
def test_words():
result = list(words('Der schnelle [„braune“] Fuchs kann keine 3,14 Meter springen, oder?'))
expected = ['Der', 'schnelle', 'braune', 'Fuchs', 'kann', 'keine', '3,14', 'Meter', 'springen', 'oder']
assert result == expected
def test_words_private_use_area():
result = list(words(
'ber die vielen Sorgen wegen deelben vergaß Hartkopf, der Frau Amtmnnin das ver⸗\n'
'ſproene zu berliefern.'))
expected = [
'ber', 'die', 'vielen', 'Sorgen', 'wegen', 'deelben', 'vergaß', 'Hartkopf',
'der', 'Frau', 'Amtmnnin', 'das', 'ver',
'ſproene', 'zu', 'berliefern']
assert result == expected
def test_word_error_rate():
assert word_error_rate('Dies ist ein Beispielsatz!', 'Dies ist ein Beispielsatz!') == 0
assert word_error_rate('Dies. ist ein Beispielsatz!', 'Dies ist ein Beispielsatz!') == 0
assert word_error_rate('Dies. ist ein Beispielsatz!', 'Dies ist ein Beispielsatz.') == 0
assert word_error_rate('Dies ist ein Beispielsatz!', 'Dies ist ein Beispielsarz:') == 1/4
assert word_error_rate('Dies ist ein Beispielsatz!', 'Dies ein ist Beispielsatz!') == 2/4
assert word_error_rate('Dies ist ein Beispielsatz!', '') == 4/4
assert math.isinf(word_error_rate('', 'Dies ist ein Beispielsatz!'))
assert word_error_rate('', '') == 0
assert word_error_rate('Schlyñ lorem ipsum dolor sit amet,', 'Schlym̃ lorem ipsum dolor sit amet.') == 1/6

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from itertools import zip_longest
from typing import Iterable
import colorama
import os
def diffprint(x, y):
"""Print elements or lists x and y, with differences in red"""
def _diffprint(x, y):
if x != y:
print(colorama.Fore.RED, x, y, colorama.Fore.RESET)
else:
print(x, y)
if isinstance(x, Iterable):
for xe, ye in zip_longest(x, y):
_diffprint(xe, ye)
else:
_diffprint(x, y)
def unzip(l):
return zip(*l)
class working_directory:
"""Context manager to temporarily change the working directory"""
def __init__(self, wd):
self.wd = wd
def __enter__(self):
self.old_wd = os.getcwd()
os.chdir(self.wd)
def __exit__(self, etype, value, traceback):
os.chdir(self.old_wd)

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from __future__ import division
import unicodedata
import uniseg.wordbreak
from .edit_distance import levenshtein
def words(s):
# Patch uniseg.wordbreak.word_break to deal with our private use characters. See also
# https://www.unicode.org/Public/UCD/latest/ucd/auxiliary/WordBreakProperty.txt
old_word_break = uniseg.wordbreak.word_break
def new_word_break(c, index=0):
if 0xE000 <= ord(c) <= 0xF8FF: # Private Use Area
return 'ALetter'
else:
return old_word_break(c, index)
uniseg.wordbreak.word_break = new_word_break
# Check if c is an unwanted character, i.e. whitespace, punctuation, or similar
def unwanted(c):
# See https://www.fileformat.info/info/unicode/category/index.htm
# and https://unicodebook.readthedocs.io/unicode.html#categories
unwanted_categories = 'O', 'M', 'P', 'Z', 'S'
unwanted_subcategories = 'Cc', 'Cf'
subcat = unicodedata.category(c)
cat = subcat[0]
return cat in unwanted_categories or subcat in unwanted_subcategories
# We follow Unicode Standard Annex #29 on Unicode Text Segmentation here: Split on word boundaries using
# uniseg.wordbreak.words() and ignore all "words" that contain only whitespace, punctation "or similar characters."
for word in uniseg.wordbreak.words(s):
if all(unwanted(c) for c in word):
pass
else:
yield word
def words_normalized(s):
return words(unicodedata.normalize('NFC', s))
def word_error_rate(reference, compared):
if isinstance(reference, str):
reference_seq = list(words_normalized(reference))
compared_seq = list(words_normalized(compared))
else:
reference_seq = list(reference)
compared_seq = list(compared)
d = levenshtein(reference_seq, compared_seq)
if d == 0:
return 0
n = len(reference_seq)
if n == 0:
return float('inf')
return d / n

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from .main import *
from .ocrd_cli import *

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{
"version": "0.0.1",
"tools": {
"ocrd-sbb-textline-detector": {
"executable": "ocrd-sbb-textline-detector",
"description": "Detect lines",
"steps": ["layout/segmentation/line"],
"input_file_grp": [
"OCR-D-IMG"
],
"output_file_grp": [
"OCR-D-SBB-SEG-LINE"
],
"parameters": {
"model": {"type": "string", "format": "file", "cacheable": true}
}
}
}
}

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qurator/sbb_textline_detector/ocrd_cli.py
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import json
import os
import tempfile
import click
import ocrd_models.ocrd_page
from ocrd import Processor
from ocrd.decorators import ocrd_cli_options, ocrd_cli_wrap_processor
from ocrd_modelfactory import page_from_file
from ocrd_models import OcrdFile
from ocrd_models.ocrd_page_generateds import MetadataItemType, LabelsType, LabelType
from ocrd_utils import concat_padded, getLogger, MIMETYPE_PAGE
from pkg_resources import resource_string
from qurator.sbb_textline_detector import textline_detector
log = getLogger('processor.OcrdSbbTextlineDetectorRecognize')
OCRD_TOOL = json.loads(resource_string(__name__, 'ocrd-tool.json').decode('utf8'))
@click.command()
@ocrd_cli_options
def ocrd_sbb_textline_detector(*args, **kwargs):
return ocrd_cli_wrap_processor(OcrdSbbTextlineDetectorRecognize, *args, **kwargs)
TOOL = 'ocrd_sbb_textline_detector'
class OcrdSbbTextlineDetectorRecognize(Processor):
def __init__(self, *args, **kwargs):
kwargs['ocrd_tool'] = OCRD_TOOL['tools'][TOOL]
kwargs['version'] = OCRD_TOOL['version']
super(OcrdSbbTextlineDetectorRecognize, self).__init__(*args, **kwargs)
def _make_file_id(self, input_file, input_file_grp, n):
file_id = input_file.ID.replace(input_file_grp, self.output_file_grp)
if file_id == input_file.ID:
file_id = concat_padded(self.output_file_grp, n)
return file_id
def _resolve_image_file(self, input_file: OcrdFile) -> str:
if input_file.mimetype == MIMETYPE_PAGE:
pcgts = page_from_file(self.workspace.download_file(input_file))
page = pcgts.get_Page()
image_file = page.imageFilename
else:
image_file = input_file.local_filename
return image_file
def process(self):
for n, page_id in enumerate(self.workspace.mets.physical_pages):
input_file = self.workspace.mets.find_files(fileGrp=self.input_file_grp, pageId=page_id)[0]
log.info("INPUT FILE %i / %s", n, input_file)
file_id = self._make_file_id(input_file, self.input_file_grp, n)
# Process the files
try:
os.mkdir(self.output_file_grp)
except FileExistsError:
pass
with tempfile.TemporaryDirectory() as tmp_dirname:
# Segment the image
image_file = self._resolve_image_file(input_file)
model = self.parameter['model']
x = textline_detector(image_file, tmp_dirname, file_id, model)
x.run()
# Read segmentation results
tmp_filename = os.path.join(tmp_dirname, file_id) + '.xml'
tmp_pcgts = ocrd_models.ocrd_page.parse(tmp_filename)
tmp_page = tmp_pcgts.get_Page()
# Create a new PAGE file from the input file
pcgts = page_from_file(self.workspace.download_file(input_file))
page = pcgts.get_Page()
# Merge results → PAGE file
page.set_PrintSpace(tmp_page.get_PrintSpace())
page.set_ReadingOrder(tmp_page.get_ReadingOrder())
page.set_TextRegion(tmp_page.get_TextRegion())
# Save metadata about this operation
metadata = pcgts.get_Metadata()
metadata.add_MetadataItem(
MetadataItemType(type_="processingStep",
name=self.ocrd_tool['steps'][0],
value=TOOL,
Labels=[LabelsType(
externalModel="ocrd-tool",
externalId="parameters",
Label=[LabelType(type_=name, value=self.parameter[name])
for name in self.parameter.keys()])]))
self.workspace.add_file(
ID=file_id,
file_grp=self.output_file_grp,
pageId=page_id,
mimetype='application/vnd.prima.page+xml',
local_filename=os.path.join(self.output_file_grp, file_id) + '.xml',
content=ocrd_models.ocrd_page.to_xml(pcgts)
)
if __name__ == '__main__':
ocrd_sbb_textline_detector()