PageNet: Towards End-to-End Weakly Supervised Page-Level Handwritten Chinese Text Recognition

• URL: http://arxiv.org/abs/2207.14807v1
• Date: Fri, 29 Jul 2022 17:47:45 GMT
• Title: PageNet: Towards End-to-End Weakly Supervised Page-Level Handwritten Chinese Text Recognition
• Authors: Dezhi Peng, Lianwen Jin, Yuliang Liu, Canjie Luo, Songxuan Lai
• Abstract summary: We propose PageNet for end-to-end weakly supervised page-level HCTR. PageNet detects and recognizes characters and predicts the reading order between them. It can still output detection and recognition results at both the character and line levels.
• Score: 44.70246958636773
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Handwritten Chinese text recognition (HCTR) has been an active research topic for decades. However, most previous studies solely focus on the recognition of cropped text line images, ignoring the error caused by text line detection in real-world applications. Although some approaches aimed at page-level text recognition have been proposed in recent years, they either are limited to simple layouts or require very detailed annotations including expensive line-level and even character-level bounding boxes. To this end, we propose PageNet for end-to-end weakly supervised page-level HCTR. PageNet detects and recognizes characters and predicts the reading order between them, which is more robust and flexible when dealing with complex layouts including multi-directional and curved text lines. Utilizing the proposed weakly supervised learning framework, PageNet requires only transcripts to be annotated for real data; however, it can still output detection and recognition results at both the character and line levels, avoiding the labor and cost of labeling bounding boxes of characters and text lines. Extensive experiments conducted on five datasets demonstrate the superiority of PageNet over existing weakly supervised and fully supervised page-level methods. These experimental results may spark further research beyond the realms of existing methods based on connectionist temporal classification or attention. The source code is available at https://github.com/shannanyinxiang/PageNet.

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