An HMM-Based Approach for Off-Line Unconstrained Handwritten Word Modeling and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gabor-Based Kernel PCA with Fractional Power Polynomial Models for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Offline Recognition of Unconstrained Handwritten Texts Using HMMs and Statistical Language Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gabor Feature Extraction for Character Recognition: Comparison with Gradient Feature
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
A review on Gabor wavelets for face recognition
Pattern Analysis & Applications
Corpus-based HIT-MW database for offline recognition of general-purpose Chinese handwritten text
International Journal on Document Analysis and Recognition
Gabor filters-based feature extraction for character recognition
Pattern Recognition
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Segmentation-free recognizer is presented to transcribe Chinese handwritten documents, incorporating Gabor features and Hidden Markov Models (HMMs). Textline is extracted and filtered as Gabor observations by sliding windows first. Then Baum-Welch algorithm is used to train character HMMs. Finally, best character string in maximizing a posteriori criterion is found out through Viterbi algorithm as output. Experiments are conducted on a collection of Chinese handwriting. The results not only show the evident feasibility of segmentation-free strategy, but also manifest the advantages of Gabor filters in the transcription of Chinese handwriting.