RVAFM: Re-parameterizing Vertical Attention Fusion Module for Handwritten Paragraph Text Recognition
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916644245209088 |
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| author | Zheng, Jinhui Liu, Zhiquan Si, Yain-Whar Li, Jianqing Zhang, Xinyuan Li, Xiaofan Huang, Haozhi Gong, Xueyuan |
| author_facet | Zheng, Jinhui Liu, Zhiquan Si, Yain-Whar Li, Jianqing Zhang, Xinyuan Li, Xiaofan Huang, Haozhi Gong, Xueyuan |
| contents | Handwritten Paragraph Text Recognition (HPTR) is a challenging task in Computer Vision, requiring the transformation of a paragraph text image, rich in handwritten text, into text encoding sequences. One of the most advanced models for this task is Vertical Attention Network (VAN), which utilizes a Vertical Attention Module (VAM) to implicitly segment paragraph text images into text lines, thereby reducing the difficulty of the recognition task. However, from a network structure perspective, VAM is a single-branch module, which is less effective in learning compared to multi-branch modules. In this paper, we propose a new module, named Re-parameterizing Vertical Attention Fusion Module (RVAFM), which incorporates structural re-parameterization techniques. RVAFM decouples the structure of the module during training and inference stages. During training, it uses a multi-branch structure for more effective learning, and during inference, it uses a single-branch structure for faster processing. The features learned by the multi-branch structure are fused into the single-branch structure through a special fusion method named Re-parameterization Fusion (RF) without any loss of information. As a result, we achieve a Character Error Rate (CER) of 4.44% and a Word Error Rate (WER) of 14.37% on the IAM paragraph-level test set. Additionally, the inference speed is slightly faster than VAN. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_03104 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RVAFM: Re-parameterizing Vertical Attention Fusion Module for Handwritten Paragraph Text Recognition Zheng, Jinhui Liu, Zhiquan Si, Yain-Whar Li, Jianqing Zhang, Xinyuan Li, Xiaofan Huang, Haozhi Gong, Xueyuan Computer Vision and Pattern Recognition Artificial Intelligence Handwritten Paragraph Text Recognition (HPTR) is a challenging task in Computer Vision, requiring the transformation of a paragraph text image, rich in handwritten text, into text encoding sequences. One of the most advanced models for this task is Vertical Attention Network (VAN), which utilizes a Vertical Attention Module (VAM) to implicitly segment paragraph text images into text lines, thereby reducing the difficulty of the recognition task. However, from a network structure perspective, VAM is a single-branch module, which is less effective in learning compared to multi-branch modules. In this paper, we propose a new module, named Re-parameterizing Vertical Attention Fusion Module (RVAFM), which incorporates structural re-parameterization techniques. RVAFM decouples the structure of the module during training and inference stages. During training, it uses a multi-branch structure for more effective learning, and during inference, it uses a single-branch structure for faster processing. The features learned by the multi-branch structure are fused into the single-branch structure through a special fusion method named Re-parameterization Fusion (RF) without any loss of information. As a result, we achieve a Character Error Rate (CER) of 4.44% and a Word Error Rate (WER) of 14.37% on the IAM paragraph-level test set. Additionally, the inference speed is slightly faster than VAN. |
| title | RVAFM: Re-parameterizing Vertical Attention Fusion Module for Handwritten Paragraph Text Recognition |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2503.03104 |