Toward Zero-shot Character Recognition: A Gold Standard Dataset with Radical-level Annotations

• URL: http://arxiv.org/abs/2308.00655v1
• Date: Tue, 1 Aug 2023 16:41:30 GMT
• Title: Toward Zero-shot Character Recognition: A Gold Standard Dataset with Radical-level Annotations
• Authors: Xiaolei Diao, Daqian Shi, Jian Li, Lida Shi, Mingzhe Yue, Ruihua Qi, Chuntao Li, Hao Xu
• Abstract summary: In this paper, we construct an ancient Chinese character image dataset that contains both radical-level and character-level annotations. To increase the adaptability of ACCID, we propose a splicing-based synthetic character algorithm to augment the training samples and apply an image denoising method to improve the image quality.
• Score: 5.761679637905164
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Optical character recognition (OCR) methods have been applied to diverse tasks, e.g., street view text recognition and document analysis. Recently, zero-shot OCR has piqued the interest of the research community because it considers a practical OCR scenario with unbalanced data distribution. However, there is a lack of benchmarks for evaluating such zero-shot methods that apply a divide-and-conquer recognition strategy by decomposing characters into radicals. Meanwhile, radical recognition, as another important OCR task, also lacks radical-level annotation for model training. In this paper, we construct an ancient Chinese character image dataset that contains both radical-level and character-level annotations to satisfy the requirements of the above-mentioned methods, namely, ACCID, where radical-level annotations include radical categories, radical locations, and structural relations. To increase the adaptability of ACCID, we propose a splicing-based synthetic character algorithm to augment the training samples and apply an image denoising method to improve the image quality. By introducing character decomposition and recombination, we propose a baseline method for zero-shot OCR. The experimental results demonstrate the validity of ACCID and the baseline model quantitatively and qualitatively.

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