用单一神经网络同时完成手写表单字符检测与分类，提升识别率。
Intelligent Character Recognition of Handwritten Forms with Deep Neural Networks
- 统一检测与分类任务，避免人工标注数据
- 在真实考试表单上达到88.28%识别率
- 适合需要端到端处理手写表单的场景
手写表单的自动处理仍具挑战性，字符检测与分类是关键步骤。本文提出一种新方法，通过深度神经网络将检测与分类合为单一任务，训练数据由原始表单和现有数据集自动生成，无需人工标注。实验表明，该单任务方法优于当前主流的两阶段方法。研究聚焦手写拉丁字母，使用EMNIST数据集，但发现其存在局限，需进一步优化。最终在真实考试表单数据上实现了88.28%的整体识别率。
原文摘要 · Abstract (English)
The automatic processing of handwritten forms remains a challenging task, wherein detection and subsequent classification of handwritten characters are essential steps. We describe a novel approach, in which both steps -- detection and classification -- are executed in one task through a deep neural network. Therefore, training data is not annotated by hand, but manufactured artificially from the underlying forms and yet existing datasets. It can be demonstrated that this single-task approach is superior in comparison to the state-of-the-art two-task approach. The current study focuses on hand-written Latin letters and employs the EMNIST data set. However, limitations were identified with this data set, necessitating further customization. Finally, an overall recognition rate of 88.28 percent was attained on real data obtained from a written exam.
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