Abstract
Real-time character recognition systems are in huge demand due to the technological developments in the areas of humanoid robots, autonomous vehicles, voice-based assistive systems, natural language processing, image processing and many more. In this paper, design of a real-time character recognition system using Histogram of Gradient features and Support Vector Machine (SVM) classifier is proposed. An attempt is made to enhance the recognition accuracy by selecting the optimum HOG cell size and scaling factor of resized image for HOG feature extraction. The computation time required for each stage of the proposed real-time system is also analyzed and reported. In order to assess the performance of proposed system, samples from standard datasets are used for evaluation and their recognition accuracies are reported. Recognition accuracies ranging from 91.11% – 100.0% are obtained by the system based on the dataset and type of classifier employed.
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