Abstract
Nowadays, there is a great demand for multilingual optical character recognition (MOCR) in various web applications. And recently, Long Short-Term Memory (LSTM) networks have yielded excellent results on Latin-based printed recognition. However, it is not flexible enough to cope with challenges posed by web applications where we need to quickly get an OCR model for a certain set of languages. This paper proposes a Hybrid Model Reuse (HMR) training approach for multilingual OCR task, based on 1D bidirectional LSTM networks coupled with a model reuse scheme. Specifically, Fixed Model Reuse (FMR) scheme is analyzed and incorporated into our approach, which implicitly grabs the useful discriminative information from a fixed text generating model. Moreover, LSTM layers from pre-trained networks for unilingual OCR task are reused to initialize the weights of target networks. Experimental results show that our proposed HMR approach, without assistance of any post-processing techniques, is able to effectively accelerate the training process and finally yield higher accuracy than traditional approaches.
Supported by the National Social Science Foundation of China (Grant No: 15BGL048), the Hubei Province Science and Technology Support Project (Grant No: 2015BAA072), the National Natural Science Foundation of China (Grant No. 61672398), the Hubei Provincial Natural Science Foundation of China (Grant No: 2017CFA012), the Fundamental Research Funds for the Central Universities (WUT: 2017II39GX).
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Xie, Z., Li, L., Zhong, X., Zhong, L., Xie, Q., Xiang, J. (2018). A Hybrid Model Reuse Training Approach for Multilingual OCR. In: Hacid, H., Cellary, W., Wang, H., Paik, HY., Zhou, R. (eds) Web Information Systems Engineering – WISE 2018. WISE 2018. Lecture Notes in Computer Science(), vol 11233. Springer, Cham. https://doi.org/10.1007/978-3-030-02922-7_34
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