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Devanagari script is widely used in the Indian subcontinent in several major languages such as Hindi, Sanskrit, Marathi and Nepali. Recognition of unconstrained (Handwritten) Devanagari character is more complex due to shape of constituent strokes. Hence character recognition (CR) has been an active area of research till now and it continues to be a challenging research topic due to its diverse applicable environment. As the size of the vocabulary increases, the complexity of algorithms also increases linearly due to the need for a larger search space. Devnagari script recognition systems using Zernike moments, fuzzy rule and quadratic classifier provide less accuracy and less efficiency. Classification methods based on learning from examples have been widely applied to character recognition from the 1990s and have brought forth significant improvements of recognition accuracies. In this paper techniques like particle swarm optimization and support vector machines are implemented and compared. An android phone is used for taking input character and MATLAB software for showing the recognized Devnagari character. For the connection between android device and MATLAB we are using PHP language. The particle swarm optimization technique provides accuracy up to 90%

A Comparative Analysis of Particle Swarm Optimization and Support Vector Machines for Devnagri Character Recognition: An Android Application