Isarn Dharma Handwritten Character Recognition Using Neural Network and Support Vector Machine

In the last decade, handwritten character recognition has become one of the most attractive and challenging research areas in field of image processing and pattern recognition. In this paper, we proposed handwritten character recognition for the Isarn Dharma character. We collected the character images by scanning ancient palm leaf manuscripts. The feature extraction techniques including zoning, projection histogram, and histogram of oriented gradient (HOG) were used to extract the feature vectors. ANN and SVM were used as classifiers in character recognition, and five-fold validation was used to evaluate the recognition results. The experiment result demonstrated that SVM classifier outperformed the other methods in all feature extractions. The recognition accuracy rate through the application of HOG was outstanding, and proved slightly better than HOG applied with zoning. This study further expresses that the gradient feature like HOG significantly outperformed the statistical features, such as zoning and projection histogram.