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一年前，笔者基于开源了一个阅读顺序模型（《【文档智能】符合人类阅读顺序的文档模型-LayoutReader及非官方权重开源》），

PDF解析并结构化技术路线方案及思路，文档智能专栏

阅读顺序检测旨在捕获人类读者能够自然理解的单词序列。现有的OCR引擎通常按照从上到下、从左到右的方式排列识别到的文本行，但这并不适用于某些文档类型，如多栏模板、表格等。LayoutReader模型使用seq2seq模型捕获文本和布局信息，用于阅读顺序预测，在实验中表现出色，并显著提高了开源和商业OCR引擎在文本行排序方面的表现。

Github:https://github.com/yujunhuics/LayoutReader
权重地址:https://www.modelscope.cn/models/yujunhuinlp/LayoutReader-only-layout-large

有伙伴私信不知如何使用，笔者通过版式分析的结果，后接开源笔者开源的模型，完善这个技术链路。供参考。先看效果：

详细代码已上传：https://github.com/yujunhuics/LayoutReader/blob/main/vis.py

#!/usr/bin/env python
# _*_coding:utf-8_*_
# Author   :    Junhui Yufrom ultralytics import YOLO
import cv2
import torch
from model import LayoutLMv3ForBboxClassification
from collections import defaultdictCLS_TOKEN_ID = 0
UNK_TOKEN_ID = 3
EOS_TOKEN_ID = 2def BboxesMasks(boxes):bbox = [[0, 0, 0, 0]] + boxes + [[0, 0, 0, 0]]input_ids = [CLS_TOKEN_ID] + [UNK_TOKEN_ID] * len(boxes) + [EOS_TOKEN_ID]attention_mask = [1] + [1] * len(boxes) + [1]return {"bbox": torch.tensor([bbox]),"attention_mask": torch.tensor([attention_mask]),"input_ids": torch.tensor([input_ids]),}def decode(logits, length):logits = logits[1: length + 1, :length]orders = logits.argsort(descending=False).tolist()ret = [o.pop() for o in orders]while True:order_to_idxes = defaultdict(list)for idx, order in enumerate(ret):order_to_idxes[order].append(idx)order_to_idxes = {k: v for k, v in order_to_idxes.items() if len(v) > 1}if not order_to_idxes:breakfor order, idxes in order_to_idxes.items():idxes_to_logit = {}for idx in idxes:idxes_to_logit[idx] = logits[idx, order]idxes_to_logit = sorted(idxes_to_logit.items(), key=lambda x: x[1], reverse=True)for idx, _ in idxes_to_logit[1:]:ret[idx] = orders[idx].pop()return retdef layoutreader(bboxes):inputs = BboxesMasks(bboxes)logits = layoutreader_model(**inputs).logits.cpu().squeeze(0)orders = decode(logits, len(bboxes))return orders# report label
# id2name = {
#     0: 'Text',
#     1: 'Title',
#     2: 'Header',
#     3: 'Footer',
#     4: 'Figure',
#     5: 'Table',
#     6: 'Toc',
#     7: 'Figure caption',
#     8: 'Table caption',
#     9: 'Equation',
#     10: 'Footnote'
# }# paper label
id2name = {0: 'Text',1: 'Title',2: 'Figure',3: 'Figure caption',4: 'Table',5: 'Table caption',6: 'Header',7: 'Footer',8: 'Reference',9: 'Equation'
}color_map = {'Text': (255, 0, 255),'Title': (0, 255, 0),'Header': (125, 125, 0),'Footer': (255, 255, 0),'Figure': (0, 0, 255),'Table': (160, 32, 240),'Toc': (199, 97, 20),'Figure caption': (255, 90, 50),'Table caption': (255, 128, 0),'Equation': (255, 123, 123),'Footnote': (222, 110, 0)
}image_path = 'page_4.png'model_path = "./LayoutReader-only-layout-large"
# 下载地址：https://modelscope.cn/models/yujunhuinlp/LayoutReader-only-layout-largelayoutreader_model = LayoutLMv3ForBboxClassification.from_pretrained(model_path)layout_model = YOLO('paper-8n.pt')
# 下载地址：https://huggingface.co/qihoo360/360LayoutAnalysis
# layout_model = YOLO('report-8n.pt')result = layout_model(image_path, save=False, conf=0.45, save_crop=False, line_width=1)
print(result)img = cv2.imread(image_path)
page_h, page_w = img.shape[:2]x_scale = 1000.0 / page_w
y_scale = 1000.0 / page_hbbox_cls = result[0].boxes.cls.tolist()
xyxyes = result[0].boxes.xyxy.tolist()
confes = result[0].boxes.conf.tolist()
print(xyxyes)boxes = []
for left, top, right, bottom in xyxyes:if left < 0:left = 0if right > page_w:right = page_wif top < 0:top = 0if bottom > page_h:bottom = page_hleft = round(left * x_scale)top = round(top * y_scale)right = round(right * x_scale)bottom = round(bottom * y_scale)assert (1000 >= right >= left >= 0 and 1000 >= bottom >= top >= 0), \f'Invalid box. right: {right}, left: {left}, bottom: {bottom}, top: {top}'boxes.append([left, top, right, bottom])print(boxes)
orders = layoutreader(boxes)
print(orders)
xyxyes = [xyxyes[i] for i in orders]
bbox_cls = [bbox_cls[i] for i in orders]
confes = [confes[i] for i in orders]
print(xyxyes)for idx, b_cls, xyxy, conf in zip(range(len(xyxyes)), bbox_cls, xyxyes, confes):top_left_x, top_left_y, bottom_right_x, bottom_right_y = xyxy[0], xyxy[1], xyxy[2], xyxy[3]cv2.rectangle(img, (int(top_left_x), int(top_left_y)), (int(bottom_right_x), int(bottom_right_y)),color_map[id2name[b_cls]],2)cv2.putText(img, f"reader:{idx}--" + id2name[b_cls] + ":" + str(round(conf, 2)),(int(top_left_x), int(top_left_y) + 5),cv2.FONT_HERSHEY_SIMPLEX,1,color_map[id2name[b_cls]], 3)  # Add label text
cv2.imwrite("vis-result.jpg", img)