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Common Sense Knowledge for Handwritten Chinese Text Recognition

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Abstract

Compared to human intelligence, computers are far short of common sense knowledge which people normally acquire during the formative years of their lives. This paper investigates the effects of employing common sense knowledge as a new linguistic context in handwritten Chinese text recognition. Three methods are introduced to supplement the standard n-gram language model: embedding model, direct model, and an ensemble of these two. The embedding model uses semantic similarities from common sense knowledge to make the n-gram probabilities estimation more reliable, especially for the unseen n-grams in the training text corpus. The direct model, in turn, considers the linguistic context of the whole document to make up for the short context limit of the n-gram model. The three models are evaluated on a large unconstrained handwriting database, CASIA-HWDB, and the results show that the adoption of common sense knowledge yields improvements in recognition performance, despite the reduced concept list hereby employed.

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Notes

  1. In the case of unconstrained texts, no corpus is wide enough to contain all possible n-grams.

  2. In Chinese, a word can comprises one or multiple characters, which can explore both syntactic and semantic meaning better than a character.

  3. High-order n-gram models need much larger training corpus and higher cost of computation and memory, n usually takes no more than 5 in practice.

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Acknowledgments

This work has been supported in part by the National Basic Research Program of China (973 Program) Grant 2012CB316302, the National Natural Science Foundation of China (NSFC) Grants 60825301 and 60933010, and the Royal Society of Edinburgh (UK) and the Chinese Academy of Sciences within the China-Scotland SIPRA (Signal Image Processing Research Academy) Programme. The authors would like to thank Jia-jun Zhang for his aid in the machine translation process.

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Authors and Affiliations

  1. National Laboratory of Pattern Recognition, Institute of Automation of Chinese Academy of Sciences, Beijing, 100190, People’s Republic of China

    Qiu-Feng Wang & Cheng-Lin Liu

  2. Temasek Laboratories, National University of Singapore, Singapore, 117411, Singapore

    Erik Cambria

  3. Department of Computing Science and Mathematics, University of Stirling, Stirling, FK9 4LA, UK

    Amir Hussain

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  1. Qiu-Feng Wang
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  2. Erik Cambria
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  3. Cheng-Lin Liu
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  4. Amir Hussain
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Correspondence to Qiu-Feng Wang.

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Wang, QF., Cambria, E., Liu, CL. et al. Common Sense Knowledge for Handwritten Chinese Text Recognition. Cogn Comput 5, 234–242 (2013). https://doi.org/10.1007/s12559-012-9183-y

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