def assign_labels(self, xml_file):
XML_Parser.parse_file(xml_file)
used_segments = list()
text_list = list()
for section in self.sections:
used_segments.extend(section.segments)
if section.text() != "":
text_list.append(section.text())
for page in self.pages:
for segment in page.segments:
if segment not in used_segments and self._is_within_flow_bounds(
segment):
if segment.text():
text_list.append(segment.text())
text_list, tags, candidate_matrix = XML_Parser.generate_candidate_matrix(
text_list)
used_segments, used_rows, used_cols = [], [], []
max_distance = np.max(candidate_matrix)
min_distance = np.min(candidate_matrix)
while len(used_cols) < len(tags):
best_pos = (0, 0)
best_score = float("inf")
for row in range(candidate_matrix.shape[0]):
for col in range(candidate_matrix.shape[1]):
if row not in used_rows and col not in used_cols:
score = candidate_matrix[row][col]
if score < best_score:
best_score = score
best_pos = (row, col)
used_rows.append(best_pos[0])
used_cols.append(best_pos[1])
if best_pos[0] < len(self.sections):
for segment in self.sections[best_pos[0]].segments:
segment.tag = tags[best_pos[1]][1]
segment.prob_tag = math.exp(-10 *
(best_score - min_distance) /
(max_distance - min_distance))
used_segments.append(segment)
else:
for page in self.pages:
for segment in page.segments:
if segment not in used_segments:
if segment.text() == text_list[best_pos[0]]:
segment.tag = tags[best_pos[1]][1]
segment.prob_tag = math.exp(
-10 * (best_score - min_distance) /
(max_distance - min_distance))
def assign_labels(self, xml_file):
XML_Parser.parse_file(xml_file)
used_segments = list()
text_list = list()
for section in self.sections:
used_segments.extend(section.segments)
if section.text() != "":
text_list.append(section.text())
for page in self.pages:
for segment in page.segments:
if segment not in used_segments and self._is_within_flow_bounds(segment):
if segment.text():
text_list.append(segment.text())
text_list, tags, candidate_matrix = XML_Parser.generate_candidate_matrix(text_list)
used_segments, used_rows, used_cols = [], [], []
max_distance = np.max(candidate_matrix)
min_distance = np.min(candidate_matrix)
while len(used_cols) < len(tags):
best_pos = (0, 0)
best_score = float("inf")
for row in range(candidate_matrix.shape[0]):
for col in range(candidate_matrix.shape[1]):
if row not in used_rows and col not in used_cols:
score = candidate_matrix[row][col]
if score < best_score:
best_score = score
best_pos = (row, col)
used_rows.append(best_pos[0])
used_cols.append(best_pos[1])
if best_pos[0] < len(self.sections):
for segment in self.sections[best_pos[0]].segments:
segment.tag = tags[best_pos[1]][1]
segment.prob_tag = math.exp( -10* (best_score - min_distance) / (max_distance - min_distance) )
used_segments.append(segment)
else:
for page in self.pages:
for segment in page.segments:
if segment not in used_segments:
if segment.text() == text_list[best_pos[0]]:
segment.tag = tags[best_pos[1]][1]
segment.prob_tag = math.exp( -10* (best_score - min_distance) / (max_distance - min_distance) )
page_number=page_count + 1,
jpg=page_images[page_count])
page.find_segment_top_neighbors()
pages.append(page)
page_count += 1
fp.close()
pdfArticle = Article(pages, pdf_name)
pdfArticle.find_default_fonts()
pdfArticle.find_content_distances()
pdfArticle.save_content(style="lines")
pdfArticle.concatenate_segments()
pdfArticle.identify_num_columns()
pdfArticle.identify_sections()
pdfArticle.save_images(image_folder)
if xml_file != "":
if label_mode == "A" or label_mode == "a":
pdfArticle.assign_labels(xml_file)
pdfArticle.print_label_accuracy()
else:
feature_vecs = XML_Parser.retrieve_tags(xml_file)
feature_vecs.sort(key=lambda x: x[1])
label_assignment_gui(feature_vecs, pdfArticle)
pdfArticle.save_content(style="segments")
#pdfArticle.extract_text()
#pdfArticle.plot_stats()
pdfArticle = Article(pages, pdf_name)
pdfArticle.find_default_fonts()
pdfArticle.find_content_distances()
pdfArticle.save_content(style="lines")
pdfArticle.concatenate_segments()
pdfArticle.identify_num_columns()
pdfArticle.identify_sections()
pdfArticle.save_images(image_folder)
if xml_file != "":
if label_mode == "A" or label_mode == "a":
pdfArticle.assign_labels(xml_file)
pdfArticle.print_label_accuracy()
else:
feature_vecs = XML_Parser.retrieve_tags(xml_file)
feature_vecs.sort(key=lambda x:x[1])
label_assignment_gui(feature_vecs, pdfArticle)
pdfArticle.save_content(style="segments")
#pdfArticle.extract_text()
#pdfArticle.plot_stats()