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https://github.com/opendatalab/MinerU.git
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增加ocr版本解析功能
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@@ -0,0 +1,29 @@
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import os
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from loguru import logger
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from magic_pdf.dict2md.ocr_mkcontent import mk_nlp_markdown
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from magic_pdf.pdf_parse_by_ocr import parse_pdf_by_ocr
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def save_markdown(markdown_text, input_filepath):
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# 获取输入文件的目录
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directory = os.path.dirname(input_filepath)
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# 获取输入文件的文件名(不带扩展名)
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base_name = os.path.basename(input_filepath)
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file_name_without_ext = os.path.splitext(base_name)[0]
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# 定义输出文件的路径
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output_filepath = os.path.join(directory, f"{file_name_without_ext}.md")
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# 将Markdown文本写入.md文件
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with open(output_filepath, 'w', encoding='utf-8') as file:
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file.write(markdown_text)
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if __name__ == '__main__':
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ocr_json_file_path = r"D:\project\20231108code-clean\ocr\new\demo_4\ocr_0.json"
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pdf_info_dict = parse_pdf_by_ocr(ocr_json_file_path)
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markdown_text = mk_nlp_markdown(pdf_info_dict)
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logger.info(markdown_text)
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save_markdown(markdown_text, ocr_json_file_path)
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@@ -0,0 +1,21 @@
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def mk_nlp_markdown(pdf_info_dict: dict):
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markdown = []
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for _, page_info in pdf_info_dict.items():
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blocks = page_info.get("preproc_blocks")
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if not blocks:
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continue
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for block in blocks:
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for line in block['lines']:
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line_text = ''
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for span in line['spans']:
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content = span['content'].replace('$', '\$') # 转义$
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if span['type'] == 'inline_equation':
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content = f"${content}$"
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elif span['type'] == 'displayed_equation':
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content = f"$$\n{content}\n$$"
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line_text += content + ' '
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# 在行末添加两个空格以强制换行
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markdown.append(line_text.strip() + ' ')
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return '\n'.join(markdown)
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@@ -119,6 +119,20 @@ def _is_left_overlap(box1, box2,):
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return x0_1<=x0_2<=x1_1 and vertical_overlap_cond
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def __is_overlaps_y_exceeds_threshold(bbox1, bbox2, overlap_ratio_threshold=0.8):
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"""检查两个bbox在y轴上是否有重叠,并且该重叠区域的高度占两个bbox高度更低的那个超过80%"""
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_, y0_1, _, y1_1 = bbox1
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_, y0_2, _, y1_2 = bbox2
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overlap = max(0, min(y1_1, y1_2) - max(y0_1, y0_2))
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height1, height2 = y1_1 - y0_1, y1_2 - y0_2
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max_height = max(height1, height2)
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min_height = min(height1, height2)
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return (overlap / min_height) > overlap_ratio_threshold
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def calculate_iou(bbox1, bbox2):
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# Determine the coordinates of the intersection rectangle
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x_left = max(bbox1[0], bbox2[0])
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@@ -163,7 +177,25 @@ def calculate_overlap_area_2_minbox_area_ratio(bbox1, bbox2):
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else:
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return intersection_area / min_box_area
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def get_minbox_if_overlap_by_ratio(bbox1, bbox2, ratio):
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"""
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通过calculate_overlap_area_2_minbox_area_ratio计算两个bbox重叠的面积占最小面积的box的比例
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如果比例大于ratio,则返回小的那个bbox,
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否则返回None
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"""
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x1_min, y1_min, x1_max, y1_max = bbox1
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x2_min, y2_min, x2_max, y2_max = bbox2
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area1 = (x1_max - x1_min) * (y1_max - y1_min)
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area2 = (x2_max - x2_min) * (y2_max - y2_min)
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overlap_ratio = calculate_overlap_area_2_minbox_area_ratio(bbox1, bbox2)
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if overlap_ratio > ratio and area1 < area2:
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return bbox1
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elif overlap_ratio > ratio and area2 < area1:
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return bbox2
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else:
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return None
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def get_bbox_in_boundry(bboxes:list, boundry:tuple)-> list:
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x0, y0, x1, y1 = boundry
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new_boxes = [box for box in bboxes if box[0] >= x0 and box[1] >= y0 and box[2] <= x1 and box[3] <= y1]
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@@ -0,0 +1,46 @@
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from magic_pdf.libs.boxbase import __is_overlaps_y_exceeds_threshold
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def merge_spans(spans):
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# 按照y0坐标排序
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spans.sort(key=lambda span: span['bbox'][1])
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lines = []
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current_line = [spans[0]]
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for span in spans[1:]:
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# 如果当前的span类型为"displayed_equation" 或者 当前行中已经有"displayed_equation"
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if span['type'] == "displayed_equation" or any(s['type'] == "displayed_equation" for s in current_line):
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# 则开始新行
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lines.append(current_line)
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current_line = [span]
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continue
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# 如果当前的span与当前行的最后一个span在y轴上重叠,则添加到当前行
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if __is_overlaps_y_exceeds_threshold(span['bbox'], current_line[-1]['bbox']):
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current_line.append(span)
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else:
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# 否则,开始新行
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lines.append(current_line)
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current_line = [span]
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# 添加最后一行
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if current_line:
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lines.append(current_line)
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# 计算每行的边界框,并对每行中的span按照x0进行排序
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line_objects = []
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for line in lines:
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# 按照x0坐标排序
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line.sort(key=lambda span: span['bbox'][0])
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line_bbox = [
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min(span['bbox'][0] for span in line), # x0
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min(span['bbox'][1] for span in line), # y0
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max(span['bbox'][2] for span in line), # x1
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max(span['bbox'][3] for span in line), # y1
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]
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line_objects.append({
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"bbox": line_bbox,
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"spans": line,
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})
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return line_objects
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@@ -0,0 +1,85 @@
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import json
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from magic_pdf.libs.boxbase import get_minbox_if_overlap_by_ratio
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from magic_pdf.libs.ocr_dict_merge import merge_spans
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def read_json_file(file_path):
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with open(file_path, 'r') as f:
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data = json.load(f)
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return data
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def construct_page_component(page_id, text_blocks_preproc):
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return_dict = {
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'preproc_blocks': text_blocks_preproc,
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'page_idx': page_id
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}
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return return_dict
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def parse_pdf_by_ocr(
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ocr_json_file_path,
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start_page_id=0,
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end_page_id=None,
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):
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ocr_pdf_info = read_json_file(ocr_json_file_path)
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pdf_info_dict = {}
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end_page_id = end_page_id if end_page_id else len(ocr_pdf_info) - 1
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for page_id in range(start_page_id, end_page_id + 1):
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ocr_page_info = ocr_pdf_info[page_id]
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layout_dets = ocr_page_info['layout_dets']
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spans = []
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for layout_det in layout_dets:
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category_id = layout_det['category_id']
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allow_category_id_list = [13, 14, 15]
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if category_id in allow_category_id_list:
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x0, y0, _, _, x1, y1, _, _ = layout_det['poly']
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bbox = [int(x0), int(y0), int(x1), int(y1)]
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# 13: 'embedding', # 嵌入公式
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# 14: 'isolated', # 单行公式
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# 15: 'ocr_text', # ocr识别文本
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span = {
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'bbox': bbox,
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}
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if category_id == 13:
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span['content'] = layout_det['latex']
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span['type'] = 'inline_equation'
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elif category_id == 14:
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span['content'] = layout_det['latex']
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span['type'] = 'displayed_equation'
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elif category_id == 15:
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span['content'] = layout_det['text']
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span['type'] = 'text'
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# print(span)
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spans.append(span)
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else:
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continue
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# 合并重叠的spans
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for span1 in spans.copy():
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for span2 in spans.copy():
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if span1 != span2:
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overlap_box = get_minbox_if_overlap_by_ratio(span1['bbox'], span2['bbox'], 0.8)
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if overlap_box is not None:
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bbox_to_remove = next((span for span in spans if span['bbox'] == overlap_box), None)
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if bbox_to_remove is not None:
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spans.remove(bbox_to_remove)
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# 将spans合并成line
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lines = merge_spans(spans)
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# 目前不做block拼接,先做个结构,每个block中只有一个line,block的bbox就是line的bbox
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blocks = []
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for line in lines:
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blocks.append({
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"bbox": line['bbox'],
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"lines": [line],
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})
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# 构造pdf_info_dict
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page_info = construct_page_component(page_id, blocks)
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pdf_info_dict[f"page_{page_id}"] = page_info
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return pdf_info_dict
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