Bikash Shaw

A Segmentation Based Approach to Offline Handwritten Devanagari Word Recognition

The present paper proposes a segmentation-based approach to handwritten Devanagari word recognition. On the basis of the head line, a word image is segmented in to pseudo characters. Hidden Markov models are proposed to recognize the pseudo characters. The word level recognition is done on the basis of a string edit distance.

Offline Handwritten Devanagari Word Recognition: A Holistic Approach Based on Directional Chain Code Feature and HMM

A hidden Markov model (HMM) based approach is proposed for recognition of offline handwritten Devanagari words. The histogram of chain-code directions in the image-strips, scanned from left to right by a sliding window, is used as the feature vector. A continuous density HMM is proposed to recognize a word image. In our approach the states of the HMM are not determined a priori, but are determined automatically based on a database of handwritten word images. A handwritten word image is assumed to be a string of several image frame primitives. These are in fact the states of the proposed HMM and are found using a certain mixture distribution. One HMM is constructed for each word. To classify an unknown word image, its class conditional probability for each HMM is computed. The class that gives highest such probability is finally selected.

Offline handwritten Devanagari word recognition: A segmentation based approach

A novel segmentation based approach is proposed for recognition of offline handwritten Devanagari words. Stroke based features are used as feature vectors. A hidden Markov model is used for recognition at pseudocharacter level. The word level recognition is done on the basis of a string edit distance.

Offline handwritten Devanagari word recognition: an HMM based approach

A hidden Markov model (HMM) for recognition of handwritten Devanagari words is proposed. The HMM has the property that its states are not defined a priori, but are determined automatically based on a database of handwritten word images. A handwritten word is assumed to be a string of several stroke primitives. These are in fact the states of the proposed HMM and are found using certain mixture distributions. One HMM is constructed for each word. To classify an unknown word image, its class conditional probability for each HMM is computed. The classification scheme has been tested on a small handwritten Devanagari word database developed recently. The classification accuracy is 87.71% and 82.89% for training and test sets respectively.

Combination of Features for Efficient Recognition of Offline Handwritten Devanagari Words

In this article, we describe our recent study of a novel combination of two feature vectors for holistic recognition of offline handwritten word images. In the literature, both contour and skeleton based feature representations have been studied for offline handwriting recognition purpose. However, to the best of our knowledge, there is no such study in which combination of the two feature representations have been considered for the purpose. In the proposed recognition scheme, we use multiclass SVM as the classifier. We have implemented the proposed approach for holistic recognition of Devanagari handwritten town names and tested its performance on a large handwritten word sample database of 100 Indian town names written in Devanagari. Experimental results show sharp improvement in recognition accuracy over the use of any of the individual feature representation schemes. The proposed approach is script independent and can be used for development of a holistic handwritten word image recognition of any script.

Offline handwritten Devanagari word recognition: Information fusion at feature and classifier levels

This article presents our recent study on fusion of information at feature and classifier output levels for improved performance of offline handwritten Devanagari word recognition. We consider here two state-of-the-art features, viz., Directional Distance Distribution (DDD) and Gradient-Structural-Concavity (GSC) features along with multi-class SVM classifiers. Here, we study various combinations of DDD features along with one or more features from the GSC feature set. We experiment by presenting different combined feature vectors as input to SVM classifiers. Also, the output vectors of different SVM classifiers fed with different feature vectors are combined by another SVM classifier. The combination of the outputs of two SVMs each being fed with a different feature vector provides superior performance to the performance of a single SVM classifier fed with the combined feature vector. Experimental results are obtained on a large handwritten Devanagari word sample image database of 100 Indian town names. The recognition results on its test samples show that SVM recognition output of DDD features combined with the SVM output of GSC features improves the final recognition accuracy significantly.