Hindi handwritten word recognition using HMM and symbol tree
Proceeding of the workshop on Document Analysis and Recognition
Proceeding of the workshop on Document Analysis and Recognition
Assamese online handwritten digit recognition system using hidden Markov models
Proceeding of the workshop on Document Analysis and Recognition
Levenshtein distance metric based holistic handwritten word recognition
Proceedings of the 4th International Workshop on Multilingual OCR
Global and local features for recognition of online handwritten numerals and Tamil characters
Proceedings of the 4th International Workshop on Multilingual OCR
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We present a complete online handwritten character recognition system for Indian languages that handles the ambiguities in segmentation as well as recognition of the strokes. The recognition is based on a generative model of handwriting formation, coupled with a discriminative model for classification of strokes. Such an approach can seamlessly integrate language and script information in the generative model and deal with similar strokes using the discriminative stroke classification model. The recognition is performed in a purely bottom-up fashion, starting with the strokes, and the ambiguities at each stage are reserved and transferred to the next stage for obtaining the most probable results at each stage. We also present the results of various pre-processing, feature selection and classification studies on a large data set collected from native language writers in two different Indian languages: Malayalam and Telugu. The system achieves a stroke level accuracy of 95.78% and 95.12% on Malayalam and Telugu data, respectively. The akshara level accuracy of the system is around 78% on a corpus of 60, 492 words from 367 writers.