结合结构知识与深度模型，精准识别手写文本异常
Leveraging Structure Knowledge and Deep Models for the Detection of Abnormal Handwritten Text
- 分两阶段检测：先定位结构原型，再用形状回归网络细化
- 在两个数据集上检测准确率显著提升，新数据集已开源
- 适合研究手写识别异常检测的学者和工程师
当前，手写文本序列结构的破坏已成为制约识别任务的主要瓶颈。典型情况包括特定标记（文本交换修改）以及由删除、替换、插入等字符修改导致的文本重叠。本文提出一种结合结构知识与深度模型的两阶段检测算法。首先，从手写文本图像中粗略定位不同结构原型。基于第一阶段结果，在第二阶段采用不同策略：引入一种通过新型半监督对比训练策略训练的形状回归网络，并充分利用字符间的空间位置关系。在两个手写文本数据集上的实验表明，所提方法能显著提升检测性能。新数据集已公开于 https://github.com/Wukong90。
原文摘要 · Abstract (English)
Currently, the destruction of the sequence structure in handwritten text has become one of the main bottlenecks restricting the recognition task. The typical situations include additional specific markers (the text swapping modification) and the text overlap caused by character modifications like deletion, replacement, and insertion. In this paper, we propose a two-stage detection algorithm that combines structure knowledge and deep models for the above mentioned text. Firstly, different structure prototypes are roughly located from handwritten text images. Based on the detection results of the first stage, in the second stage, we adopt different strategies. Specifically, a shape regression network trained by a novel semi-supervised contrast training strategy is introduced and the positional relationship between the characters is fully employed. Experiments on two handwritten text datasets show that the proposed method can greatly improve the detection performance. The new dataset is available at https://github.com/Wukong90.
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