论文标题
全面的手写段落文本识别系统：词典
A Comprehensive Handwritten Paragraph Text Recognition System: LexiconNet
论文作者
论文摘要
在这项研究中，我们使用手写文本识别文献作为垂直注意网络和单词梁搜索的两种最先进的方法提出了一个有效的程序。注意模块负责内部线分割，因此以逐条方式处理页面。在解码步骤中，我们添加了一个基于连接的时间分类的单词梁搜索解码器，作为后处理步骤。在这项研究中，端到端的段落识别系统以词典解码器作为后处理。我们的过程报告标准数据集的最新结果。 IAM数据集的报告字符错误率为3.24％，提高了27.19％，在40.83％提高的犯罪率为1.13％，Read-16数据集提高了40.83％，现有文献提高了32.31％，单词误差率提高了32.31％，IAM数据集的8.29％在43.02％的数据集中提高了2.94％，RIMES的56％，56％。从现有结果中读取2016年的数据集，其增长率为47.27％。该工作中报告的字符错误率和单词错误率超过了文献中报告的结果。
In this study, we have presented an efficient procedure using two state-of-the-art approaches from the literature of handwritten text recognition as Vertical Attention Network and Word Beam Search. The attention module is responsible for internal line segmentation that consequently processes a page in a line-by-line manner. At the decoding step, we have added a connectionist temporal classification-based word beam search decoder as a post-processing step. In this study, an end-to-end paragraph recognition system is presented with a lexicon decoder as a post-processing step. Our procedure reports state-of-the-art results on standard datasets. The reported character error rate is 3.24% on the IAM dataset with 27.19% improvement, 1.13% on RIMES with 40.83% improvement and 2.43% on the READ-16 dataset with 32.31% improvement from existing literature and the word error rate is 8.29% on IAM dataset with 43.02% improvement, 2.94% on RIMES dataset with 56.25% improvement and 7.35% on READ-2016 dataset with 47.27% improvement from the existing results. The character error rate and word error rate reported in this work surpass the results reported in the literature.
