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reading order: improve handling of headings and horizontal seps

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- drop connected components analysis to test overlaps between
  horizontal separators and (horizontal) neighbours (introduced
  in ab17a927)
- instead of converting headings to topline and baseline during
  `find_number_of_columns_in_document` (introduced in 9f1595d7),
  add them to the matrix unchanged, but mark as extra type
  (besides horizontal and vertical separtors)
- convert headings to toplines and baselines no earlier than in
  `return_boxes_of_images_by_order_of_reading_new`
- for both headings and horizontal separators, if they already
  span multiple columns, check if they would overlap (horizontal)
  neighbours by looking at successively larger (left and right)
  intervals of columns (and pick the largest elongation which
  does not introduce any overlaps)
This commit is contained in:
Robert Sachunsky 2025-10-24 22:51:19 +02:00
parent 3367462d18
commit 19b2c3fa42
1 changed files with 80 additions and 47 deletions
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127
src/eynollah/utils/__init__.py
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@ -1387,8 +1387,6 @@ def return_points_with_boundies(peaks_neg_fin, first_point, last_point):
return peaks_neg_tot
def find_number_of_columns_in_document(region_pre_p, num_col_classifier, tables, label_seps, contours_h=None):
ncomps, ccomps = cv2.connectedComponents(region_pre_p.astype(np.uint8))
separators_closeup = 1 * (region_pre_p == label_seps)
separators_closeup[0:110] = 0
separators_closeup[-150:] = 0
@ -1414,14 +1412,6 @@ def find_number_of_columns_in_document(region_pre_p, num_col_classifier, tables,
dist_ye = max_ye - min_ye
if dist_ye <= 50 and dist_xe >= 3 * dist_ye:
cnts_hor_e.append(cnt)
labels = np.setdiff1d(np.unique(ccomps[med_ye]), [0])
if len(labels) == 1:
# mid line does not intersect with any other region
# so add it as extra splitter line
cnts_hor_e.append(np.array([[[0, med_ye]],
[[ccomps.shape[1], med_ye]],
[[ccomps.shape[1], med_ye + 1]],
[[0, med_ye + 1]]]))
# delete horizontal contours (leaving only the edges)
separators_closeup_n_binary = cv2.fillPoly(separators_closeup_n_binary, pts=cnts_hor_e, color=0)
@ -1493,7 +1483,7 @@ def find_number_of_columns_in_document(region_pre_p, num_col_classifier, tables,
slope_seps_org_hor=slope_seps_org_hor[dist_x_hor>=len_x/2.0]
dist_x_hor=dist_x_hor[dist_x_hor>=len_x/2.0]
matrix_of_seps_ch=np.zeros((len(cy_seps_hor)+len(cx_seps_ver),10))
matrix_of_seps_ch = np.zeros((len(cy_seps_hor)+len(cx_seps_ver), 10), dtype=int)
matrix_of_seps_ch[:len(cy_seps_hor),0]=args_hor
matrix_of_seps_ch[len(cy_seps_hor):,0]=args_ver
matrix_of_seps_ch[len(cy_seps_hor):,1]=cx_seps_ver
@ -1515,34 +1505,17 @@ def find_number_of_columns_in_document(region_pre_p, num_col_classifier, tables,
if contours_h is not None:
_, dist_x_head, x_min_head, x_max_head, cy_head, _, y_min_head, y_max_head, _ = \
find_features_of_lines(contours_h)
# matrix_l_n = np.zeros((len(cy_head), matrix_of_seps_ch.shape[1]))
# args_head = np.arange(len(cy_head))
# matrix_l_n[:, 0] = args_head
# matrix_l_n[:, 2] = x_min_head+30
# matrix_l_n[:, 3] = x_max_head-30
# matrix_l_n[:, 4] = dist_x_head
# matrix_l_n[:, 5] = y_min_head-3-8
# matrix_l_n[:, 6] = y_min_head-5-8
# matrix_l_n[:, 7] = y_max_head#y_min_head+1-8
# matrix_l_n[:, 8] = 4
# split at toplines (y_min_head) and baselines (y_max_head) instead of center (cy_head):
cy_head = np.stack((y_min_head, y_max_head)).T.flatten()
y_min_head, y_max_head = (np.stack((y_min_head - 2, y_max_head - 2)).T.flatten(),
np.stack((y_min_head + 2, y_max_head + 2)).T.flatten())
x_min_head = np.repeat(x_min_head, 2)
x_max_head = np.repeat(x_max_head, 2)
dist_x_head = np.repeat(dist_x_head, 2)
matrix_l_n = np.zeros((len(cy_head), matrix_of_seps_ch.shape[1]))
matrix_l_n = np.zeros((len(cy_head), matrix_of_seps_ch.shape[1]), dtype=int)
args_head = np.arange(len(cy_head))
matrix_l_n[:, 0] = args_head
# +/- 30px to avoid crossing col peaks by accident
matrix_l_n[:, 2] = x_min_head + 30
matrix_l_n[:, 3] = x_max_head - 30
matrix_l_n[:, 2] = x_min_head
matrix_l_n[:, 3] = x_max_head
matrix_l_n[:, 4] = dist_x_head
matrix_l_n[:, 5] = cy_head
matrix_l_n[:, 6] = y_min_head
matrix_l_n[:, 7] = y_max_head
matrix_l_n[:, 8] = 4
matrix_l_n[:, 8] = y_max_head - y_min_head
matrix_l_n[:, 9] = 2 # mark as heading (so it can be split into 2 horizontal separators as needed)
matrix_of_seps_ch = np.append(
matrix_of_seps_ch, matrix_l_n, axis=0)
@ -1551,9 +1524,12 @@ def find_number_of_columns_in_document(region_pre_p, num_col_classifier, tables,
cy_seps_splitters = np.append(cy_seps_splitters, special_separators)
if contours_h is not None:
cy_seps_splitters_head=cy_head[(x_min_head<=.16*region_pre_p.shape[1]) &
(x_max_head>=.84*region_pre_p.shape[1])]
cy_seps_splitters = np.append(cy_seps_splitters, cy_seps_splitters_head)
y_min_splitters_head = y_min_head[(x_min_head<=.16*region_pre_p.shape[1]) &
(x_max_head>=.84*region_pre_p.shape[1])]
y_max_splitters_head = y_max_head[(x_min_head<=.16*region_pre_p.shape[1]) &
(x_max_head>=.84*region_pre_p.shape[1])]
cy_seps_splitters = np.append(cy_seps_splitters, y_min_splitters_head)
cy_seps_splitters = np.append(cy_seps_splitters, y_max_splitters_head)
cy_seps_splitters = np.sort(cy_seps_splitters).astype(int)
splitter_y_new = [0] + list(cy_seps_splitters) + [region_pre_p.shape[0]]
@ -1713,17 +1689,6 @@ def return_boxes_of_images_by_order_of_reading_new(
#num_col, peaks_neg_fin = find_num_col(
# regions_without_separators[top:bot,:],
# multiplier=7.0)
x_min_hor_some=matrix_new[:,2][ (matrix_new[:,9]==0) ]
x_max_hor_some=matrix_new[:,3][ (matrix_new[:,9]==0) ]
cy_hor_some=matrix_new[:,5][ (matrix_new[:,9]==0) ]
y_max_hor_some=matrix_new[:,7][ (matrix_new[:,9]==0) ]
if right2left_readingorder:
x_max_hor_some_new = width_tot - x_min_hor_some
x_min_hor_some_new = width_tot - x_max_hor_some
x_min_hor_some =list(np.copy(x_min_hor_some_new))
x_max_hor_some =list(np.copy(x_max_hor_some_new))
peaks_neg_tot = np.array([0] + peaks_neg_fin + [width_tot])
#print(peaks_neg_tot,'peaks_neg_tot')
peaks_neg_tot_tables.append(peaks_neg_tot)
@ -1731,6 +1696,74 @@ def return_boxes_of_images_by_order_of_reading_new(
all_columns = set(range(len(peaks_neg_tot) - 1))
#print("all_columns", all_columns)
# elongate horizontal separators+headings as much as possible without overlap
args_nonver = matrix_new[:, 9] != 1
regions_with_separators = np.copy(regions_without_separators[top:bot])
for xmin, xmax, ymin, ymax in matrix_new[:, [2, 3, 6, 7]]:
regions_with_separators[ymin - top: ymax - top, xmin: xmax] = 6
# def dbg_imshow(box, title):
# xmin, xmax, ymin, ymax = box
# plt.imshow(regions_with_separators, extent=[0, width_tot, bot, top])
# plt.gca().add_patch(patches.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin,
# fill=False, linewidth=1, edgecolor='r'))
# plt.title(title + " at %d:%d, %d:%d" % (ymin, ymax, xmin, xmax))
# plt.show()
for i in np.flatnonzero(args_nonver):
xmin, xmax, ymin, ymax, typ = matrix_new[i, [2, 3, 6, 7, 9]]
cut = regions_with_separators[ymin - top: ymax - top]
# dbg_imshow([xmin, xmax, ymin, ymax], "separator %d (%s)" % (i, "heading" if typ else "horizontal"))
starting = xmin - peaks_neg_tot
min_start = np.flatnonzero(starting >= 0)[-1] # last left-of
ending = xmax - peaks_neg_tot
max_end = np.flatnonzero(ending < 0)[0] # first right-of
# skip elongation unless this is already a multi-column separator/heading:
if not max_end - min_start > 1:
continue
# is there anything left of min_start?
for j in range(min_start):
# dbg_imshow([peaks_neg_tot[j], xmin, ymin, ymax], "start of %d candidate %d" % (i, j))
if not np.any(cut[:, peaks_neg_tot[j]: xmin]):
# print("elongated sep", i, "typ", typ, "start", xmin, "to", j, peaks_neg_tot[j])
matrix_new[i, 2] = peaks_neg_tot[j] + 1 # elongate to start of this column
break
# is there anything right of max_end?
for j in range(len(peaks_neg_tot) - 1, max_end, -1):
# dbg_imshow([xmax, peaks_neg_tot[j], ymin, ymax], "end of %d candidate %d" % (i, j))
if not np.any(cut[:, xmax: peaks_neg_tot[j]]):
# print("elongated sep", i, "typ", typ, "end", xmax, "to", j, peaks_neg_tot[j])
matrix_new[i, 3] = peaks_neg_tot[j] - 1 # elongate to end of this column
break
args_hor = matrix_new[:, 9] == 0
x_min_hor_some = matrix_new[:, 2][args_hor]
x_max_hor_some = matrix_new[:, 3][args_hor]
y_max_hor_some = matrix_new[:, 7][args_hor]
cy_hor_some = matrix_new[:, 5][args_hor]
args_head = matrix_new[:, 9] == 2
x_min_hor_head = matrix_new[:, 2][args_head]
x_max_hor_head = matrix_new[:, 3][args_head]
y_min_hor_head = matrix_new[:, 6][args_head]
y_max_hor_head = matrix_new[:, 7][args_head]
cy_hor_head = matrix_new[:, 5][args_head]
# split headings at toplines (y_min_head) and baselines (y_max_head)
# instead of merely adding their center (cy_head) as horizontal separator
# (x +/- 30px to avoid crossing col peaks by accident)
x_min_hor_some = np.append(x_min_hor_some, np.tile(x_min_hor_head + 30, 2))
x_max_hor_some = np.append(x_max_hor_some, np.tile(x_max_hor_head - 30, 2))
y_max_hor_some = np.append(y_max_hor_some, # baselines
np.concatenate((y_min_hor_head + 2,
y_max_hor_head + 2)))
cy_hor_some = np.append(cy_hor_some, # toplines
np.concatenate((y_min_hor_head - 2,
y_max_hor_head - 2)))
if right2left_readingorder:
x_max_hor_some = width_tot - x_min_hor_some
x_min_hor_some = width_tot - x_max_hor_some
reading_order_type, x_starting, x_ending, y_mid, y_max, \
y_mid_without_mother, x_start_without_mother, x_end_without_mother, \
there_is_sep_with_child, \