Recognizing offline handwritten characters is a challenging problem and is considered to be more significant than the recognition of on-line handwritten characters. This study is undertaken to resolve the issue of offline handwritten character recognition for Gurmukhi script, one of the prominent scripts in the northern part of India. The Gurmukhi character images are presented using a single layer as well as multi-layer histogram of gradient features. Once the character images are represented using these features, one-against-all classification strategy, implemented through Support Vector Machine and k-Nearest Neighbour classifiers, is employed to recognize these characters. A dataset of 3500 handwritten Gurmukhi characters written by different writers is created and the scope of the Histogram Oriented Gradient (HOG) and Pyramid Histogram Oriented Gradient (PHOG) features is explored for the recognition of offline handwritten Gurmukhi characters. The simulation study reveals character recognition accuracy of 99.1% with SVM classifier for PHOG feature. The technique is robust to inter-class and intra-class variations present in the Gurmukhi script and has significant scope of application to the recognition of other scripts too.

Offline Gurmukhi Handwritten Character Recognition, Optical Character Recognition, Histogram Oriented Gradient Feature, Pyramid Histogram Oriented Gradient Feature, Support Vector Machine Classifier, k-NN Classifier

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