def area_summary(area):
summary = {}
summary['soup'] = area
# Bounding box (x1, y1, x2, y2)
summary.update(helpers.extractbbox(area.get('title')))
# Number of lines
summary['lines'] = len(area.find_all('span', 'ocr_line'))
summary['line_heights'] = []
for line in area.find_all('span', 'ocr_line'):
bbox = helpers.extractbbox(line.get('title'))
height = bbox['y2'] - bbox['y1']
summary['line_heights'].append(height)
# Number of words
summary['words'] = len(
filter(
None,
area.getText().strip().replace('\n', ' ').replace(' ',
' ').split(' ')))
# Area
summary['area'] = (summary['x2'] - summary['x1']) * (summary['y2'] -
summary['y1'])
# Get spacing of words
summary['x_gaps'] = np.zeros(summary['x2'] - summary['x1'], dtype=np.int)
# Words per line
summary['words_in_line'] = []
summary['word_distances'] = []
summary['word_heights'] = []
summary['word_areas'] = []
summary['words_per_line'] = []
# Iterate on each line in the area
for line in area.find_all('span', 'ocr_line'):
# For each line, get words
words = line.find_all('span', 'ocrx_word')
# Record the number of words in this line
summary['words_per_line'].append(len(words))
for word_idx, word in enumerate(words):
wordbbox = helpers.extractbbox(word.get('title'))
summary['word_heights'].append(wordbbox['y2'] - wordbbox['y1'])
summary['word_areas'].append((wordbbox['x2'] - wordbbox['x1']) *
(wordbbox['y2'] - wordbbox['y1']))
for x in range(wordbbox['x1'] - summary['x1'],
wordbbox['x2'] - summary['x1']):
summary['x_gaps'][x] = 1
# If word isn't the last word in a line, get distance between word and word + 1
if word_idx != (len(words) - 1):
wordP1bbox = helpers.extractbbox(words[word_idx +
1].get('title'))
# Pythagorean theorum FTW
summary['word_distances'].append(
math.sqrt(
math.pow((wordP1bbox['x1'] - wordbbox['x2']), 2) +
math.pow((wordP1bbox['y1'] - wordbbox['y1']), 2)))
# Count whitespace gaps
summary['gaps'] = helpers.get_gaps(summary['x_gaps'])
# Get the mean of the differences of the word distances (all the same == 0, difference increases away from 0)
summary['word_separation_index'] = 0 if summary[
'words'] == 0 else helpers.meanOfDifferences(summary['word_distances'])
# Quantify the variation in the height of words in this area
summary['word_height_index'] = 0 if summary[
'words'] == 0 else helpers.meanOfDifferences(summary['word_heights'])
# Get the average word height of this area
summary['word_height_avg'] = 0 if summary['words'] == 0 else np.nanmean(
summary['word_heights'])
# Get word/area ratio
summary['word_area_index'] = 0 if summary['words'] == 0 else np.sum(
summary['word_areas']) / float(summary['area'])
return summary
def area_summary_offset(area):
summary = {}
summary.update(area)
coords = area.get('data-coordinates')
spl = coords.split(' ')
origin_x1 = int(spl[0])
origin_y1 = int(spl[1])
# Number of lines
summary['lines'] = len(summary['soup'].find_all('span', 'ocr_line'))
summary['line_heights'] = []
for line in summary['soup'].find_all('span', 'ocr_line'):
bbox = helpers.extractbbox(line.get('title'))
height = bbox['y2'] - bbox['y1']
summary['line_heights'].append(height)
# Number of words
try:
summary['words'] = len(list(filter(None, summary['soup'].getText().strip().replace('\n', ' ').replace(' ', ' ').split(' '))))
except:
summary['words'] = 0
# Area
summary['area'] = (summary['x2'] - summary['x1']) * (summary['y2'] - summary['y1'])
# Get spacing of words
summary['x_gaps'] = np.zeros(summary['x2'] - summary['x1'], dtype=np.int)
# Words per line
summary['words_in_line'] = []
summary['word_distances'] = []
summary['word_heights'] = []
summary['word_areas'] = []
summary['words_per_line'] = []
# Record the x position of the first word in each line
summary['first_word_x'] = []
# Iterate on each line in the area
for line in summary['soup'].find_all('span', 'ocr_line'):
# For each line, get words
words = line.find_all('span', 'ocrx_word')
# Record the number of words in this line
summary['words_per_line'].append(len(words))
for word_idx, word in enumerate(words):
wordbbox = helpers.extractbbox(word.get('title'))
word_area = (wordbbox['x2'] - wordbbox['x1']) * (wordbbox['y2'] - wordbbox['y1'])
if word_area > summary['area'] or \
wordbbox['x2'] > summary['x2'] or \
wordbbox['x1'] < summary['x1'] or \
wordbbox['y1'] < summary['y1'] or \
wordbbox['y2'] > summary['y2']:
print("Word outside of the enclosing area! Tesseract's black box strikes again!")
continue
# Record the x coordinate of the first word of each line
if word_idx == 0:
summary['first_word_x'] = wordbbox['x1'] + coords[0]
summary['word_heights'].append(wordbbox['y2'] - wordbbox['y1'])
summary['word_areas'].append(word_area)
for x in range(wordbbox['x1'] - summary['x1'], wordbbox['x2'] - summary['x1']):
summary['x_gaps'][x] = 1
# If word isn't the last word in a line, get distance between word and word + 1
if word_idx != (len(words) - 1):
wordP1bbox = helpers.extractbbox(words[ word_idx + 1 ].get('title'))
# Pythagorean theorum FTW
summary['word_distances'].append(math.sqrt(math.pow((wordP1bbox['x1'] - wordbbox['x2']), 2) + math.pow((wordP1bbox['y1'] - wordbbox['y1']), 2)))
# Count whitespace gaps
summary['gaps'] = helpers.get_gaps(summary['x_gaps'])
# Get the mean of the differences of the word distances (all the same == 0, difference increases away from 0)
summary['word_separation_index'] = 0 if summary['words'] == 0 else helpers.meanOfDifferences(summary['word_distances'])
# Quantify the variation in the height of words in this area
summary['word_height_index'] = 0 if summary['words'] == 0 else helpers.meanOfDifferences(summary['word_heights'])
# Get the average word height of this area
summary['word_height_avg'] = 0 if summary['words'] == 0 else np.nanmean(summary['word_heights'])
# Get word/area ratio
summary['word_area_index'] = 0 if summary['words'] == 0 else np.sum(summary['word_areas']) / float(summary['area'])
return summary