Maximum Entropy Regularization and Chinese Text Recognition

Cheng, Changxu; Xu, Wuheng; Bai, Xiang; Feng, Bin; Liu, Wenyu

doi:10.1007/978-3-030-57058-3_1

Cited by 4 publications

(1 citation statement)

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“…According to our observations, only few methods are specifically designed for Chinese text recognition although it has enormous application markets all over the world. In particular, the existing Chinese text methods mainly aim at handwriting text recognition [13,14,19,28,76,79] or handwriting character recognition [9,11,77,80,84,94,95,97] thanks to the detailed specifications formulated by the CASIA-HWDB database [39]. For example, Xiao et al [81] put forward a template-instance loss to distinguish the similar Chinese characters pairs in the feature domain.…”

Section: Existing Text Recognition Methodsmentioning

confidence: 99%

Benchmarking Chinese Text Recognition: Datasets, Baselines, and an Empirical Study

Chen¹,

Yu²,

Ma³

et al. 2021

Preprint

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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 for English texts, whereas ignoring the pivotal role of Chinese texts. As another widely-spoken language, Chinese text recognition in all ways has extensive application markets. Based on our observations, we attribute the scarce attention on Chinese text recognition to the lack of reasonable dataset construction standards, unified evaluation methods, and results of the existing baselines. To fill this gap, we manually collect Chinese text datasets from publicly available competitions, projects, and papers, then divide them into four categories including scene, web, document, and handwriting datasets. Furthermore, we evaluate a series of representative text recognition methods on these datasets with unified evaluation methods to provide experimental results. By analyzing the experimental results, we surprisingly observe that state-of-the-art baselines for recognizing English texts cannot perform well on Chinese scenarios. We consider that there still remain numerous challenges under exploration due to the characteristics of Chinese texts, which are quite different from English texts. The code and datasets are made publicly available at https://github.com/ FudanVI/benchmarking-chinese-text-recognition. Figure 1. Three reasons for the scarce attention of Chinese text recognition. (a) People may use different ways to crop text regions, which leads to unfair comparison. (b) It is necessary to specify the equivalence between lowercase and uppercase, half-width and full-width, simplified and traditional characters. (c) The existing methods are mainly evaluated with English datasets rather than Chinese datasets.

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