🤖 AI Summary
This study addresses the challenging task of character detection and recognition in handwritten forms, which remains difficult due to structural complexity and heavy reliance on extensive manual annotations. The authors propose an end-to-end deep neural network approach that unifies character detection and classification into a single task, thereby eliminating the dependency on manually annotated data inherent in conventional two-stage pipelines. By synthesizing training data using the EMNIST dataset combined with realistic form layouts, the model achieves strong generalization without requiring real-world labeled examples. Evaluated on actual handwritten examination forms, the method attains an overall character recognition accuracy of 88.28%, substantially outperforming existing two-stage approaches.
📝 Abstract
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.