首页|基于两阶段深度学习的表格结构识别方法

基于两阶段深度学习的表格结构识别方法

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鉴于在图像中识别表格结构面临着表格样式众多、图像质量各异等难题,提出一种融合表格线与文字块信息的两阶段深度学习框架,以实现少线复杂表格结构的识别.首先,将残差结构引入U-Net语义分割网络中,增强网络传递表格线信息能力,完成表格线的识别;然后,加入文字块位置信息以提高模型识别无线或少线表格结构的能力.该方法在PubTabNet数据集上的树编辑距离(tree-edit-distance similarity,TEDS)评分达到95.95.实验证明,该方法在识别少线表或无线表时表现优秀,并能高效、准确地识别存在合并单元格的复杂结构表格.
Table Structure Recognition Method Based on Two-stage Deep Learning
In view of the difficulties of table structures recognition in images,such as numerous table styles and different image quality,a two-stage deep learning framework integrating table lines and text block information was proposed to realize the recognition of complex table structures with few lines.Firstly,the residual structure was introduced into the U-Net semantic segmentation network to enhance the network transmission ability of table line information and complete the recognition of table line.Then,the text block location information was added to improve the ability of the model to recognize wirelessor less linear table structures.The TEDS(tree-edit-distance similarity)score of this method was 95.95 on PubTabNet dataset.The experimental results suggested that the proposed method performed well in recognition of few line tables or wireless tables,and could efficiently and accurately recognize the complex structure tables with merged cells.

table recognitionsemantic segmentationdeep learning

孙寅生、袁贞明

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杭州师范大学信息科学与技术学院,浙江杭州 311121

表格识别 语义分割 深度学习

浙江省自然科学基金项目国家卫生健康委科学研究基金项目新疆生产建设兵团重点领域科技攻关项目

LGF20F020009WKJ-ZJ-22152021AB034-2

2024

杭州师范大学学报(自然科学版)
杭州师范大学

杭州师范大学学报(自然科学版)

CSTPCD
影响因子:0.386
ISSN:1674-232X
年,卷(期):2024.23(3)
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