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@@ -128,8 +128,13 @@ def fill_char_in_spans(spans, all_chars):
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span['chars'].append(char)
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break
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+ empty_spans = []
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+
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for span in spans:
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chars_to_content(span)
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+ if len(span['content']) == 0:
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+ empty_spans.append(span)
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+ return empty_spans
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# 使用鲁棒性更强的中心点坐标判断
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@@ -162,21 +167,6 @@ def calculate_char_in_span(char_bbox, span_bbox, char_is_line_stop_flag):
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def txt_spans_extract_v2(pdf_page, spans, all_bboxes, all_discarded_blocks, lang):
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- useful_spans = []
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- unuseful_spans = []
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- for span in spans:
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- for block in all_bboxes:
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- if block[7] in [BlockType.ImageBody, BlockType.TableBody, BlockType.InterlineEquation]:
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- continue
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- else:
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- if calculate_overlap_area_in_bbox1_area_ratio(span['bbox'], block[0:4]) > 0.5:
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- useful_spans.append(span)
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- break
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- for block in all_discarded_blocks:
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- if calculate_overlap_area_in_bbox1_area_ratio(span['bbox'], block[0:4]) > 0.5:
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- unuseful_spans.append(span)
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- break
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-
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text_blocks = pdf_page.get_text('rawdict', flags=fitz.TEXTFLAGS_TEXT)['blocks']
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# @todo: 拿到char之后把倾斜角度较大的先删一遍
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@@ -186,24 +176,29 @@ def txt_spans_extract_v2(pdf_page, spans, all_bboxes, all_discarded_blocks, lang
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for span in line['spans']:
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all_pymu_chars.extend(span['chars'])
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- new_spans = []
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+ useful_spans = []
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+ unuseful_spans = []
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+ for span in spans:
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+ for block in all_bboxes + all_discarded_blocks:
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+ if block[7] in [BlockType.ImageBody, BlockType.TableBody, BlockType.InterlineEquation]:
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+ continue
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+ overlap_ratio = calculate_overlap_area_in_bbox1_area_ratio(span['bbox'], block[0:4])
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+ if overlap_ratio > 0.5:
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+ if block in all_bboxes:
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+ useful_spans.append(span)
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+ else:
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+ unuseful_spans.append(span)
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+ break
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- for span in useful_spans:
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- if span['type'] in [ContentType.Text]:
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- span['chars'] = []
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- new_spans.append(span)
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+ new_spans = []
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- for span in unuseful_spans:
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+ for span in useful_spans + unuseful_spans:
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if span['type'] in [ContentType.Text]:
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span['chars'] = []
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new_spans.append(span)
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- fill_char_in_spans(new_spans, all_pymu_chars)
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+ empty_spans = fill_char_in_spans(new_spans, all_pymu_chars)
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- empty_spans = []
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- for span in new_spans:
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- if len(span['content']) == 0:
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- empty_spans.append(span)
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if len(empty_spans) > 0:
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# 初始化ocr模型
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@@ -216,18 +211,14 @@ def txt_spans_extract_v2(pdf_page, spans, all_bboxes, all_discarded_blocks, lang
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)
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for span in empty_spans:
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- spans.remove(span)
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- # 对span的bbox截图
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+ # 对span的bbox截图再ocr
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span_img = cut_image_to_pil_image(span['bbox'], pdf_page, mode="cv2")
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ocr_res = ocr_model.ocr(span_img, det=False)
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- # logger.info(f"ocr_res: {ocr_res}")
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- # logger.info(f"empty_span: {span}")
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if ocr_res and len(ocr_res) > 0:
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if len(ocr_res[0]) > 0:
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ocr_text, ocr_score = ocr_res[0][0]
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if ocr_score > 0.5 and len(ocr_text) > 0:
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- span['content'] = ocr_text
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- spans.append(span)
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+ span['content'] = ocr_text
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return spans
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