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This research introduces a unified deep neural network architecture that jointly performs detection and classification for handwritten forms, eliminating the need for manual annotation by synthesizing training data from existing datasets. The approach outperforms traditional two-stage methods, achieving an 88.28% recognition rate on real-world exam data, though it currently requires further customization for non-Latin scripts. For practitioners in computer vision, the key takeaway is the efficiency gain from a single-task design that reduces labeling overhead while maintaining high accuracy on specific domains.
Intelligent Character Recognition of Handwritten Forms with Deep Neural Networks
arXiv:2606.08858v1 Announce Type: cross 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.
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.
Author's accepted manuscript of a published Springer book chapter. 14 pages, 16 figures
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
arXiv:2606.08858 [cs.CV]
(or arXiv:2606.08858v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.08858 arXiv-issued DOI via DataCite (pending registration)
In: Cavallucci D., Livotov P., Brad S. (eds), Towards AI-Aided Invention and Innovation, IFIP Advances in Information and Communication Technology, vol. 682, Springer Nature Switzerland, 2023, pp. 81-94
https://doi.org/10.1007/978-3-031-42532-5_6 DOI(s) linking to related resources
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