Modified Quadratic Discriminant Functions and the Application to Chinese Character Recognition
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Corpus-based HIT-MW database for offline recognition of general-purpose Chinese handwritten text
International Journal on Document Analysis and Recognition
Normalization-Cooperated Gradient Feature Extraction for Handwritten Character Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A hybrid post-processing system for offline handwritten Chinese script recognition
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Integrating Language Model in Handwritten Chinese Text Recognition
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
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HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
CASIA Online and Offline Chinese Handwriting Databases
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
An Iterative Relative Entropy Minimization-Based Data Selection Approach for n-Gram Model Adaptation
IEEE Transactions on Audio, Speech, and Language Processing
Improving Handwritten Chinese Text Recognition by Unsupervised Language Model Adaptation
DAS '12 Proceedings of the 2012 10th IAPR International Workshop on Document Analysis Systems
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DAS '12 Proceedings of the 2012 10th IAPR International Workshop on Document Analysis Systems
Handwritten Chinese Text Recognition by Integrating Multiple Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Use of contexts in language model interpolation and adaptation
Computer Speech and Language
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Hybrid Language Model for Handwritten Chinese Sentence Recognition
ICFHR '12 Proceedings of the 2012 International Conference on Frontiers in Handwriting Recognition
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This paper presents an effective approach for unsupervised language model adaptation (LMA) using multiple models in offline recognition of unconstrained handwritten Chinese texts. The domain of the document to recognize is variable and usually unknown a priori, so we use a two-pass recognition strategy with a pre-defined multi-domain language model set. We propose three methods to dynamically generate an adaptive language model to match the text output by first-pass recognition: model selection, model combination and model reconstruction. In model selection, we use the language model with minimum perplexity on the first-pass recognized text. By model combination, we learn the combination weights via minimizing the sum of squared error with both L2-norm and L1-norm regularization. For model reconstruction, we use a group of orthogonal bases to reconstruct a language model with the coefficients learned to match the document to recognize. Moreover, we reduce the storage size of multiple language models using two compression methods of split vector quantization (SVQ) and principal component analysis (PCA). Comprehensive experiments on two public Chinese handwriting databases CASIA-HWDB and HIT-MW show that the proposed unsupervised LMA approach improves the recognition performance impressively, particularly for ancient domain documents with the recognition accuracy improved by 7 percent. Meanwhile, the combination of the two compression methods largely reduces the storage size of language models with little loss of recognition accuracy.