融合区域特征和表格线识别的医学化验单布局识别方法研究

Research on Medical Laboratory Sheet Lay-out Recognition Method Combining Regional Features and Form Line Recognition

为解决医学化验单版式繁多,通用文字识别系统输出正确率低,识别结果黏连严重的问题,在医学化验单文字识别全过程中,借鉴文档版面分析的思路,提出医学化验单进行布局识别的研究,以提高文字识别的输出正确率。针对医学化验单布局上属于少线表和其呈明显区域分布的特点,本文提出融合区域特征和表格线识别的医学化验单布局识别算法。首先,利用基于Unet的表格线提取网络,提前化验单图像的表格线。其次,引入Mask R-CNN区域特征提取网络,将表格线特征与原始化验单图像一同作为网络的输入。最后,实现对上述区域特征与表格线特征的融合,对待识别区域之间的关系进行建模,并生成最终的精确坐标和语义标签。实验表明,本算法能够较为明显提高医学化验单布局识别的准确度。

In order to solve the problem that the general character recognition system has a low output accuracy rate due to the large number of formats of medical test sheets, and the recognition results are severely stuck. In the whole process of text recognition of medical test sheets, drawing lessons from the idea of document layout analysis, research on the layout recognition of medical test sheets is proposed to improve the output accuracy of text recognition. Aiming at the characteristics of a few-line tables and obvious regional distribution in the layout of medical test sheets, this paper proposes a medical test sheet layout recognition algorithm that integrates regional features and table line recognition. First, use the Unet-based table line extraction network to test the table line of the single image in advance. Second, the Mask R-CNN regional feature extraction network is introduced, and the table line features and the original test sheet image are used as the input of the network. Finally, the fusion of the above-mentioned regional features and table line features is realized, the relationship between the regions to be recognized is modeled, and the final precise coordinates and semantic labels are generated. Experiments show that this algorithm can significantly improve the accuracy of medical test sheet layout recognition.