A Survey on Feature Extraction Methods & Classifiers for Handwritten Gurmukhi Character Recognition
DOI:
https://doi.org/10.26438/ijcse/v7i2.313320Keywords:
Handwritten Gurmukhi Character Recognition, Feature Extraction, SIFT, Classification Methods, Classification Methods, ConvNetAbstract
Offline Handwritten Character Recognition is the trending application of computer vision in machine learning. Though a large amount of work has already been done in Handwritten Gurmukhi Character recognition, but still in a belief to get better accuracy with state of the art algorithm like deep convolution neural networks. Any character recognition process consists of five stages i.e. digitization, pre-processing, segmentation, feature extraction and Classifier. Feature Extraction is one of the significant stage in the process because extracted features of one character differentiate it from another character. In this paper, various techniques have been summarized which are used to extract the feature of digitized character image and various classifiers used mainly in character recognition.
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