Reading Chinese in Natural Scenes with a Bag-of-Radicals Prior
Liu Yongbin, Liu Qingjie, Chen Jiaxin, Wang Yunhong

TL;DR
This paper introduces a novel radical-embedding approach for Chinese scene text recognition, leveraging ideographic descriptions and multi-task training to significantly improve performance over traditional methods.
Contribution
The paper proposes a radical-embedding representation and multi-task training strategy to enhance Chinese scene text recognition accuracy.
Findings
Model with RE + CVFM + multi-task training outperforms baselines on six datasets.
Radical-based supervision improves ideographic structure perception.
Significant performance gains on Chinese STR datasets.
Abstract
Scene text recognition (STR) on Latin datasets has been extensively studied in recent years, and state-of-the-art (SOTA) models often reach high accuracy. However, the performance on non-Latin transcripts, such as Chinese, is not satisfactory. In this paper, we collect six open-source Chinese STR datasets and evaluate a series of classic methods performing well on Latin datasets, finding a significant performance drop. To improve the performance on Chinese datasets, we propose a novel radical-embedding (RE) representation to utilize the ideographic descriptions of Chinese characters. The ideographic descriptions of Chinese characters are firstly converted to bags of radicals and then fused with learnable character embeddings by a character-vector-fusion-module (CVFM). In addition, we utilize a bag of radicals as supervision signals for multi-task training to improve the ideographic…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsHandwritten Text Recognition Techniques · Natural Language Processing Techniques · Topic Modeling
