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@@ -0,0 +1,206 @@
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+from sklearn.cluster import DBSCAN
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+import numpy as np
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+from loguru import logger
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+
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+from magic_pdf.libs.boxbase import _is_in
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+
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+
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+LINE_STOP_FLAG = ['.', '!', '?', '。', '!', '?',":", ":", ")", ")", ";"]
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+INLINE_EQUATION = 'inline_equation'
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+INTER_EQUATION = "displayed_equation"
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+TEXT = "text"
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+
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+def __add_line_period(blocks, layout_bboxes):
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+ """
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+ 为每行添加句号
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+ 如果这个行
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+ 1. 以行内公式结尾,但没有任何标点符号,此时加个句号,认为他就是段落结尾。
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+ """
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+ for block in blocks:
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+ for line in block['lines']:
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+ last_span = line['spans'][-1]
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+ span_type = last_span['type']
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+ if span_type in [TEXT, INLINE_EQUATION]:
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+ span_content = last_span['content'].strip()
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+ if span_type==INLINE_EQUATION and span_content[-1] not in LINE_STOP_FLAG:
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+ if span_type in [INLINE_EQUATION, INTER_EQUATION]:
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+ last_span['content'] = span_content + '.'
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+
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+
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+
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+def __valign_lines(blocks, layout_bboxes):
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+ """
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+ 对齐行的左侧和右侧。
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+ 扫描行的左侧和右侧,如果x0, x1差距不超过3就强行对齐到所处layout的左右两侧(和layout有一段距离)。
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+ 3是个经验值,TODO,计算得来
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+
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+ """
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+
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+ min_distance = 3
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+ min_sample = 2
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+
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+ for layout_box in layout_bboxes:
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+ blocks_in_layoutbox = [b for b in blocks if _is_in(b['bbox'], layout_box['layout_bbox'])]
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+ if len(blocks_in_layoutbox)==0:
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+ continue
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+
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+ x0_lst = np.array([[line['bbox'][0], 0] for block in blocks_in_layoutbox for line in block['lines']])
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+ x1_lst = np.array([[line['bbox'][2], 0] for block in blocks_in_layoutbox for line in block['lines']])
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+ x0_clusters = DBSCAN(eps=min_distance, min_samples=min_sample).fit(x0_lst)
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+ x1_clusters = DBSCAN(eps=min_distance, min_samples=min_sample).fit(x1_lst)
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+ x0_uniq_label = np.unique(x0_clusters.labels_)
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+ x1_uniq_label = np.unique(x1_clusters.labels_)
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+
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+ x0_2_new_val = {} # 存储旧值对应的新值映射
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+ x1_2_new_val = {}
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+ for label in x0_uniq_label:
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+ if label==-1:
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+ continue
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+ x0_index_of_label = np.where(x0_clusters.labels_==label)
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+ x0_raw_val = x0_lst[x0_index_of_label][:,0]
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+ x0_new_val = np.min(x0_lst[x0_index_of_label][:,0])
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+ x0_2_new_val.update({idx: x0_new_val for idx in x0_raw_val})
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+ for label in x1_uniq_label:
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+ if label==-1:
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+ continue
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+ x1_index_of_label = np.where(x1_clusters.labels_==label)
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+ x1_raw_val = x1_lst[x1_index_of_label][:,0]
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+ x1_new_val = np.max(x1_lst[x1_index_of_label][:,0])
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+ x1_2_new_val.update({idx: x1_new_val for idx in x1_raw_val})
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+
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+ for block in blocks_in_layoutbox:
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+ for line in block['lines']:
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+ x0, x1 = line['bbox'][0], line['bbox'][2]
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+ if x0 in x0_2_new_val:
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+ line['bbox'][0] = int(x0_2_new_val[x0])
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+
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+ if x1 in x1_2_new_val:
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+ line['bbox'][2] = int(x1_2_new_val[x1])
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+ # 其余对不齐的保持不动
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+
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+ # 由于修改了block里的line长度,现在需要重新计算block的bbox
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+ for block in blocks_in_layoutbox:
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+ block['bbox'] = [min([line['bbox'][0] for line in block['lines']]),
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+ min([line['bbox'][1] for line in block['lines']]),
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+ max([line['bbox'][2] for line in block['lines']]),
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+ max([line['bbox'][3] for line in block['lines']])]
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+
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+
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+def __common_pre_proc(blocks, layout_bboxes):
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+ """
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+ 不分语言的,对文本进行预处理
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+ """
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+ __add_line_period(blocks, layout_bboxes)
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+ __valign_lines(blocks, layout_bboxes)
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+
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+
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+def __pre_proc_zh_blocks(blocks, layout_bboxes):
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+ """
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+ 对中文文本进行分段预处理
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+ """
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+ pass
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+
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+
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+def __pre_proc_en_blocks(blocks, layout_bboxes):
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+ """
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+ 对英文文本进行分段预处理
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+ """
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+ pass
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+
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+
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+def __group_line_by_layout(blocks, layout_bboxes, lang="en"):
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+ """
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+ 每个layout内的行进行聚合
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+ """
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+ # 因为只是一个block一行目前, 一个block就是一个段落
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+ lines_group = []
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+
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+ for lyout in layout_bboxes:
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+ lines = [line for block in blocks if _is_in(block['bbox'], lyout['layout_bbox']) for line in block['lines']]
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+ lines_group.append(lines)
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+
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+ return lines_group
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+
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+
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+def __split_para_in_layoutbox(lines_group, layout_bboxes, lang="en", char_avg_len=10):
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+ """
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+ lines_group 进行行分段——layout内部进行分段。
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+ 1. 先计算每个group的左右边界。
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+ 2. 然后根据行末尾特征进行分段。
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+ 末尾特征:以句号等结束符结尾。并且距离右侧边界有一定距离。
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+
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+ """
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+ def get_span_text(span):
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+ c = span.get('content', '')
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+ if len(c)==0:
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+ c = span.get('image-path', '')
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+
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+ return c
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+
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+ paras = []
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+ right_tail_distance = 1.5 * char_avg_len
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+ for lines in lines_group:
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+ if len(lines)==0:
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+ continue
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+ layout_right = max([line['bbox'][2] for line in lines])
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+ para = [] # 元素是line
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+ for line in lines:
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+ line_text = ''.join([get_span_text(span) for span in line['spans']])
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+ #logger.info(line_text)
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+ last_span_type = line['spans'][-1]['type']
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+ if last_span_type in [TEXT, INLINE_EQUATION]:
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+ last_char = line['spans'][-1]['content'][-1]
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+ if last_char in LINE_STOP_FLAG or line['bbox'][2] < layout_right - right_tail_distance:
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+ para.append(line)
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+ paras.append(para)
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+ # para_text = ''.join([span['content'] for line in para for span in line['spans']])
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+ # logger.info(para_text)
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+ para = []
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+ else:
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+ para.append(line)
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+ else: # 其他,图片、表格、行间公式,各自占一段
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+ para.append(line)
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+ paras.append(para)
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+ # para_text = ''.join([get_span_text(span) for line in para for span in line['spans']])
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+ # logger.info(para_text)
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+ para = []
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+ if len(para)>0:
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+ paras.append(para)
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+ # para_text = ''.join([get_span_text(span) for line in para for span in line['spans']])
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+ # logger.info(para_text)
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+ para = []
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+
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+ return paras
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+
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+
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+def __do_split(blocks, layout_bboxes, lang="en"):
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+ """
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+ 根据line和layout情况进行分段
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+ 先实现一个根据行末尾特征分段的简单方法。
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+ """
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+ """
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+ 算法思路:
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+ 1. 扫描layout里每一行,找出来行尾距离layout有边界有一定距离的行。
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+ 2. 从上述行中找到末尾是句号等可作为断行标志的行。
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+ 3. 参照上述行尾特征进行分段。
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+ 4. 图、表,目前独占一行,不考虑分段。
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+ """
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+ lines_group = __group_line_by_layout(blocks, layout_bboxes, lang) # block内分段
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+ layout_paras = __split_para_in_layoutbox(lines_group, layout_bboxes, lang) # block间连接分段
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+
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+ return layout_paras
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+
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+
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+def para_split(blocks, layout_bboxes, lang="en"):
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+ """
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+ 根据line和layout情况进行分段
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+ """
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+ __common_pre_proc(blocks, layout_bboxes)
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+ if lang=='en':
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+ __do_split(blocks, layout_bboxes, lang)
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+ elif lang=='zh':
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+ __do_split(blocks, layout_bboxes, lang)
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+
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+ splited_blocks = __do_split(blocks, layout_bboxes, lang)
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+
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+ return splited_blocks
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