From 9dff8a78babb35a110ae496b0e90b885363c8279 Mon Sep 17 00:00:00 2001
From: Clemens Neudecker <clemens.neudecker@gmail.com>
Date: Wed, 20 Nov 2019 00:27:10 +0100
Subject: [PATCH] Update Preprocessing.md

---
 docs/Preprocessing.md | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/docs/Preprocessing.md b/docs/Preprocessing.md
index 3c8521e..b9b04e3 100644
--- a/docs/Preprocessing.md
+++ b/docs/Preprocessing.md
@@ -3,15 +3,19 @@
 The preprocessing pipeline that is developed at the 
 [Berlin State Library](http://staatsbibliothek-berlin.de/) 
 comprises the following steps:
-- Layout Analysis & Textline Extraction @[sbb_pixelwise_segmentation](https://github.com/qurator-spk/pixelwise_segmentation_SBB)
-- OCR & Word Segmentation @[ocrd_tesserocr](https://github.com/OCR-D/ocrd_tesserocr)
-- Tokenization
-- Named Entity Recognition @[sbb_ner](https://github.com/qurator-spk/sbb_ner)
 
 ### Layout Analysis & Textline Extraction
 
+Layout Analysis & Textline Extraction @[sbb_pixelwise_segmentation](https://github.com/qurator-spk/pixelwise_segmentation_SBB)
+
 ### OCR & Word Segmentation
 
+OCR is based on [OCR-D](https://github.com/OCR-D)'s [ocrd_tesserocr](https://github.com/OCR-D/ocrd_tesserocr) which requires [Tesseract](https://github.com/tesseract-ocr/tesseract) **>= 4.1.0**. The [Fraktur_5000000](https://ub-backup.bib.uni-mannheim.de/~stweil/ocrd-train/data/Fraktur_5000000/) model, which is trained on [GT4HistOCR](https://github.com/tesseract-ocr/tesstrain/wiki/GT4HistOCR) is used. 
+corpiu
+The [PAGE-XML](https://github.com/PRImA-Research-Lab/PAGE-XML) produced by the [Layout Analysis & Textline Extraction](https://github.com/qurator-spk/neath/blob/master/docs/Preprocessing.md#layout-analysis--textline-extraction) is taken as input, and the output is [PAGE-XML](https://github.com/PRImA-Research-Lab/PAGE-XML) containing the text recognition results with absolute pixel coordinates describing bounding boxes for words. 
+
 ### Tokenization
 
 ### Named Entity Recognition
+
+For Named Entity Recognition, a [BERT-Base](https://github.com/google-research/bert) model was trained. [sbb_ner](https://github.com/qurator-spk/sbb_ner) is using a combination of unsupervised training on a large (~2.3m pages) OCR corpus in combination with supervised training on a small (50k tokens) annotated corpus. Further details are available in the [paper](https://corpora.linguistik.uni-erlangen.de/data/konvens/proceedings/papers/KONVENS2019_paper_4.pdf).