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improve rules governing column split

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- reduce `sigma` for smoothing of input to `find_peaks`
  (so we get deeper gaps between columns)
- allow column boundaries closer to the margins
  (50 instead of 100 or 200 px, 170 instead of 370 px)
- allow column boundaries closer to each other
  (300 instead of 400 px)
- add a secondary `grenze` criterion for depth of gap
  (relative to lowest minimum, if that is smaller than
   the old criterion relative to lowest maximum)
- for calls to `find_num_col` within parts of a page,
  do allow unbalanced column boundaries
This commit is contained in:
Robert Sachunsky 2025-11-14 03:39:36 +01:00
parent 4abc2ff572
commit 4475183f08
1 changed files with 66 additions and 47 deletions
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113
src/eynollah/utils/__init__.py
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@ -241,7 +241,7 @@ def find_num_col_deskew(regions_without_separators, sigma_, multiplier=3.8):
z = gaussian_filter1d(regions_without_separators_0, sigma_) z = gaussian_filter1d(regions_without_separators_0, sigma_)
return np.std(z) return np.std(z)
def find_num_col(regions_without_separators, num_col_classifier, tables, multiplier=3.8): def find_num_col(regions_without_separators, num_col_classifier, tables, multiplier=3.8, unbalanced=False):
if not regions_without_separators.any(): if not regions_without_separators.any():
return 0, [] return 0, []
regions_without_separators_0 = regions_without_separators.sum(axis=0) regions_without_separators_0 = regions_without_separators.sum(axis=0)
@ -249,13 +249,15 @@ def find_num_col(regions_without_separators, num_col_classifier, tables, multipl
# ax1.imshow(regions_without_separators, aspect="auto") # ax1.imshow(regions_without_separators, aspect="auto")
# ax2.plot(regions_without_separators_0) # ax2.plot(regions_without_separators_0)
# plt.show() # plt.show()
sigma_ = 35 # 70#35 sigma_ = 25 # 70#35
meda_n_updown = regions_without_separators_0[::-1] meda_n_updown = regions_without_separators_0[::-1]
first_nonzero = next((i for i, x in enumerate(regions_without_separators_0) if x), 0) first_nonzero = next((i for i, x in enumerate(regions_without_separators_0) if x), 0)
last_nonzero = next((i for i, x in enumerate(meda_n_updown) if x), 0) last_nonzero = next((i for i, x in enumerate(meda_n_updown) if x), 0)
last_nonzero = len(regions_without_separators_0) - last_nonzero last_nonzero = len(regions_without_separators_0) - last_nonzero
last_nonzero = last_nonzero - 100 last_nonzero = last_nonzero - 50 #- 100
first_nonzero = first_nonzero + 200 first_nonzero = first_nonzero + 50 #+ 200
last_offmargin = len(regions_without_separators_0) - 170 #370
first_offmargin = 170 #370
y = regions_without_separators_0 # [first_nonzero:last_nonzero] y = regions_without_separators_0 # [first_nonzero:last_nonzero]
y_help = np.zeros(len(y) + 20) y_help = np.zeros(len(y) + 20)
y_help[10 : len(y) + 10] = y y_help[10 : len(y) + 10] = y
@ -285,26 +287,34 @@ def find_num_col(regions_without_separators, num_col_classifier, tables, multipl
# ax2.axvline(last_nonzero, label="last nonzero") # ax2.axvline(last_nonzero, label="last nonzero")
# ax2.text(first_nonzero, 0, "first nonzero", rotation=90) # ax2.text(first_nonzero, 0, "first nonzero", rotation=90)
# ax2.text(last_nonzero, 0, "last nonzero", rotation=90) # ax2.text(last_nonzero, 0, "last nonzero", rotation=90)
# ax2.axvline(370, label="first") # ax2.axvline(first_offmargin, label="first offmargin")
# ax2.axvline(len(y) - 370, label="last") # ax2.axvline(last_offmargin, label="last offmargin")
# ax2.text(370, 0, "first", rotation=90) # ax2.text(first_offmargin, 0, "first offmargin", rotation=90)
# ax2.text(len(y) - 370, 0, "last", rotation=90) # ax2.text(last_offmargin, 0, "last offmargin", rotation=90)
# plt.show() # plt.show()
peaks_neg = peaks_neg - 10 - 10 peaks_neg = peaks_neg - 10 - 10
# print("raw peaks", peaks)
peaks = peaks[(peaks > 0.06 * len(y)) & peaks = peaks[(peaks > 0.06 * len(y)) &
(peaks < 0.94 * len(y))] (peaks < 0.94 * len(y))]
# print("non-marginal peaks", peaks)
interest_pos = z[peaks] interest_pos = z[peaks]
# print("interest_pos", interest_pos)
interest_pos = interest_pos[interest_pos > 10] interest_pos = interest_pos[interest_pos > 10]
if not interest_pos.any(): if not interest_pos.any():
return 0, [] return 0, []
# plt.plot(z) # plt.plot(z)
# plt.show() # plt.show()
#print("raw peaks_neg", peaks_neg)
peaks_neg = peaks_neg[(peaks_neg > first_nonzero) & peaks_neg = peaks_neg[(peaks_neg > first_nonzero) &
(peaks_neg < last_nonzero)] (peaks_neg < last_nonzero)]
peaks_neg = peaks_neg[(peaks_neg > 370) & #print("non-zero peaks_neg", peaks_neg)
(peaks_neg < len(y) - 370)] peaks_neg = peaks_neg[(peaks_neg > first_offmargin) &
(peaks_neg < last_offmargin)]
#print("non-marginal peaks_neg", peaks_neg)
interest_neg = z[peaks_neg] interest_neg = z[peaks_neg]
#print("interest_neg", interest_neg)
if not interest_neg.any(): if not interest_neg.any():
return 0, [] return 0, []
@ -317,10 +327,14 @@ def find_num_col(regions_without_separators, num_col_classifier, tables, multipl
min_peaks_neg = 0 # np.min(interest_neg) min_peaks_neg = 0 # np.min(interest_neg)
# cutoff criterion: fixed fraction of lowest column height
dis_talaei = (min_peaks_pos - min_peaks_neg) / multiplier dis_talaei = (min_peaks_pos - min_peaks_neg) / multiplier
grenze = min_peaks_pos - dis_talaei grenze = min_peaks_pos - dis_talaei
#np.mean(y[peaks_neg[0]:peaks_neg[-1]])-np.std(y[peaks_neg[0]:peaks_neg[-1]])/2.0 #np.mean(y[peaks_neg[0]:peaks_neg[-1]])-np.std(y[peaks_neg[0]:peaks_neg[-1]])/2.0
# extra criterion: fixed multiple of lowest gap height
grenze = min(grenze, multiplier * (5 + np.min(interest_neg)))
# print(interest_neg,'interest_neg') # print(interest_neg,'interest_neg')
# print(grenze,'grenze') # print(grenze,'grenze')
# print(min_peaks_pos,'min_peaks_pos') # print(min_peaks_pos,'min_peaks_pos')
@ -356,18 +370,20 @@ def find_num_col(regions_without_separators, num_col_classifier, tables, multipl
# print(peaks_neg_fin,'peaks_neg_fin') # print(peaks_neg_fin,'peaks_neg_fin')
# print(num_col,'diz') # print(num_col,'diz')
# cancel if resulting split is highly unbalanced across available width # cancel if resulting split is highly unbalanced across available width
if ((num_col == 3 and if unbalanced:
((peaks_neg_fin[0] > 0.75 * len(y) and pass
peaks_neg_fin[1] > 0.75 * len(y)) or elif ((num_col == 3 and
(peaks_neg_fin[0] < 0.25 * len(y) and ((peaks_neg_fin[0] > 0.75 * len(y) and
peaks_neg_fin[1] < 0.25 * len(y)) or peaks_neg_fin[1] > 0.75 * len(y)) or
(peaks_neg_fin[0] < 0.5 * len(y) - 200 and (peaks_neg_fin[0] < 0.25 * len(y) and
peaks_neg_fin[1] < 0.5 * len(y)) or peaks_neg_fin[1] < 0.25 * len(y)) or
(peaks_neg_fin[0] > 0.5 * len(y) + 200 and (peaks_neg_fin[0] < 0.5 * len(y) - 200 and
peaks_neg_fin[1] > 0.5 * len(y)))) or peaks_neg_fin[1] < 0.5 * len(y)) or
(num_col == 2 and (peaks_neg_fin[0] > 0.5 * len(y) + 200 and
(peaks_neg_fin[0] > 0.75 * len(y) or peaks_neg_fin[1] > 0.5 * len(y)))) or
peaks_neg_fin[0] < 0.25 * len(y)))): (num_col == 2 and
(peaks_neg_fin[0] > 0.75 * len(y) or
peaks_neg_fin[0] < 0.25 * len(y)))):
num_col = 1 num_col = 1
peaks_neg_fin = [] peaks_neg_fin = []
@ -376,7 +392,7 @@ def find_num_col(regions_without_separators, num_col_classifier, tables, multipl
# filter out peaks that are too close (<400px) to each other: # filter out peaks that are too close (<400px) to each other:
# among each group, pick the position with smallest amount of text # among each group, pick the position with smallest amount of text
diff_peaks = np.abs(np.diff(peaks_neg_fin)) diff_peaks = np.abs(np.diff(peaks_neg_fin))
cut_off = 400 cut_off = 300 #400
peaks_neg_true = [] peaks_neg_true = []
forest = [] forest = []
# print(len(peaks_neg_fin),'len_') # print(len(peaks_neg_fin),'len_')
@ -401,30 +417,32 @@ def find_num_col(regions_without_separators, num_col_classifier, tables, multipl
#print(peaks_neg_true, "peaks_neg_true") #print(peaks_neg_true, "peaks_neg_true")
##print(num_col,'early') ##print(num_col,'early')
# cancel if resulting split is highly unbalanced across available width # cancel if resulting split is highly unbalanced across available width
if ((num_col == 3 and if unbalanced:
((peaks_neg_true[0] > 0.75 * len(y) and pass
peaks_neg_true[1] > 0.75 * len(y)) or elif ((num_col == 3 and
(peaks_neg_true[0] < 0.25 * len(y) and ((peaks_neg_true[0] > 0.75 * len(y) and
peaks_neg_true[1] < 0.25 * len(y)) or peaks_neg_true[1] > 0.75 * len(y)) or
(peaks_neg_true[0] < 0.5 * len(y) - 200 and (peaks_neg_true[0] < 0.25 * len(y) and
peaks_neg_true[1] < 0.5 * len(y)) or peaks_neg_true[1] < 0.25 * len(y)) or
(peaks_neg_true[0] > 0.5 * len(y) + 200 and (peaks_neg_true[0] < 0.5 * len(y) - 200 and
peaks_neg_true[1] > 0.5 * len(y)))) or peaks_neg_true[1] < 0.5 * len(y)) or
(num_col == 2 and (peaks_neg_true[0] > 0.5 * len(y) + 200 and
(peaks_neg_true[0] > 0.75 * len(y) or peaks_neg_true[1] > 0.5 * len(y)))) or
peaks_neg_true[0] < 0.25 * len(y)))): (num_col == 2 and
(peaks_neg_true[0] > 0.75 * len(y) or
peaks_neg_true[0] < 0.25 * len(y)))):
num_col = 1 num_col = 1
peaks_neg_true = [] peaks_neg_true = []
if (num_col == 3 and elif (num_col == 3 and
(peaks_neg_true[0] < 0.75 * len(y) and (peaks_neg_true[0] < 0.75 * len(y) and
peaks_neg_true[0] > 0.25 * len(y) and peaks_neg_true[0] > 0.25 * len(y) and
peaks_neg_true[1] > 0.80 * len(y))): peaks_neg_true[1] > 0.80 * len(y))):
num_col = 2 num_col = 2
peaks_neg_true = [peaks_neg_true[0]] peaks_neg_true = [peaks_neg_true[0]]
if (num_col == 3 and elif (num_col == 3 and
(peaks_neg_true[1] < 0.75 * len(y) and (peaks_neg_true[1] < 0.75 * len(y) and
peaks_neg_true[1] > 0.25 * len(y) and peaks_neg_true[1] > 0.25 * len(y) and
peaks_neg_true[0] < 0.20 * len(y))): peaks_neg_true[0] < 0.20 * len(y))):
num_col = 2 num_col = 2
peaks_neg_true = [peaks_neg_true[1]] peaks_neg_true = [peaks_neg_true[1]]
@ -1151,8 +1169,8 @@ def order_of_regions(textline_mask, contours_main, contours_head, y_ref, x_ref):
##matrix_of_orders[:len_main,4]=final_indexers_sorted[:] ##matrix_of_orders[:len_main,4]=final_indexers_sorted[:]
# assert len(final_indexers_sorted) == len(contours_main) + len(contours_head) assert len(set(final_indexers_sorted)) == len(contours_main) + len(contours_head)
# assert not len(final_indexers_sorted) or max(final_index_type) == max(len(contours_main) assert set(final_index_type) == set(range(len(contours_main))).union(range(len(contours_head)))
return np.array(final_indexers_sorted), np.array(final_types), np.array(final_index_type) return np.array(final_indexers_sorted), np.array(final_types), np.array(final_index_type)
@ -1518,7 +1536,8 @@ def return_boxes_of_images_by_order_of_reading_new(
regions_without_separators[top:bot], regions_without_separators[top:bot],
# we do not expect to get all columns in small parts (headings etc.): # we do not expect to get all columns in small parts (headings etc.):
num_col_classifier if bot - top >= big_part else 1, num_col_classifier if bot - top >= big_part else 1,
tables, multiplier=6. if erosion_hurts else 7.) tables, multiplier=6. if erosion_hurts else 7.,
unbalanced=True)
except: except:
peaks_neg_fin=[] peaks_neg_fin=[]
num_col = 0 num_col = 0
@ -1534,7 +1553,7 @@ def return_boxes_of_images_by_order_of_reading_new(
if len(peaks_neg_fin)==0: if len(peaks_neg_fin)==0:
num_col, peaks_neg_fin = find_num_col( num_col, peaks_neg_fin = find_num_col(
regions_without_separators[top:bot], regions_without_separators[top:bot],
num_col_classifier, tables, multiplier=3.) num_col_classifier, tables, multiplier=3., unbalanced=True)
#print(peaks_neg_fin,'peaks_neg_fin') #print(peaks_neg_fin,'peaks_neg_fin')
peaks_neg_fin_early = [0] + peaks_neg_fin + [width_tot-1] peaks_neg_fin_early = [0] + peaks_neg_fin + [width_tot-1]