Offline Handwritten Hindi Numerals Recognition using Zernike Moments
Feature selection mechanism plays a vital role in attaining higher recognition rate in machine based character recognition systems. Zernike moments and Zernike complex moments are found to be very beneficial as a basic tool to generate shape descriptors of a digital image, as they are capable of representing image features efficiently. These moments have been successfully utilized to solve some of the image processing problems and possess the property of rotation invariance which is a desirable aspect in many applications requiring a proficient offline character recognition system. This paper is devoted for inspecting the efficacy of Zernike moments and Zernike complex moments for offline Hindi numerals recognition. Feature vectors consisting of Zernike moments and Zernike complex moments are supplied to the resilient backpropagation neural network for training and different strategies are considered for the performance evaluation of these moments in the study. Character recognition rate of approx. 80.8% and 94.8% is attained through Zernike moments and Zernike complex moments, respectively, which establishes the supremacy of Zernike complex moments over Zernike moments for offline Hindi numerals character recognition.
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