Benchmarking Chinese Text Recognition: Datasets, Baselines, and an Empirical Study
This work addresses the problem of limited resources and benchmarks for Chinese text recognition, which is incremental as it standardizes existing datasets and methods rather than introducing new techniques.
The authors tackled the lack of standardized benchmarks for Chinese text recognition by collecting and categorizing datasets, establishing evaluation protocols, and providing baseline results, showing that existing methods underperform compared to English due to language differences and that radical-level supervision can boost performance.
The flourishing blossom of deep learning has witnessed the rapid development of text recognition in recent years. However, the existing text recognition methods are mainly proposed for English texts. As another widely-spoken language, Chinese text recognition (CTR) in all ways has extensive application markets. Based on our observations, we attribute the scarce attention on CTR to the lack of reasonable dataset construction standards, unified evaluation protocols, and results of the existing baselines. To fill this gap, we manually collect CTR datasets from publicly available competitions, projects, and papers. According to application scenarios, we divide the collected datasets into four categories including scene, web, document, and handwriting datasets. Besides, we standardize the evaluation protocols in CTR. With unified evaluation protocols, we evaluate a series of representative text recognition methods on the collected datasets to provide baselines. The experimental results indicate that the performance of baselines on CTR datasets is not as good as that on English datasets due to the characteristics of Chinese texts that are quite different from the Latin alphabet. Moreover, we observe that by introducing radical-level supervision as an auxiliary task, the performance of baselines can be further boosted. The code and datasets are made publicly available at https://github.com/FudanVI/benchmarking-chinese-text-recognition