Pen Stroke Digit Recognition Using CNN
DOI:
https://doi.org/10.32628/CSEIT24102103Keywords:
CNN, KNN, OCR, Naïve Bayes, Neural Networks, Feature Extraction, Optical Character Recognition, Segmentation, Handwritten Digit ClassificationAbstract
Hand-written character and digit recognition have been one of the most exigent and engrossing field of pattern recognition and image processing. The main aim of this paper is to demonstrate and represent the work which is related to hand-written digit recognition. The hand-written digit recognition is a very exigent task. In this recognition task, the numbers are not accurately written or scripted as they differ in shape or size; due to which the feature extraction and segmentation of hand-written numerical script is arduous. The vertical and horizontal projections methods are used for the purpose of segmentation in the proposed work. KNN is applied for recognition and classification. The digit recognition is a very exigent task. In this recognition task, the numbers are not accurately written or scripted as they differ in shape or size; due to which the feature extraction and segmentation of numerical script is arduous. The vertical and horizontal projections methods are used for the purpose of segmentation in the proposed work. CNN is applied for recognition, classification and we are also getting nice accuracy while doing prediction.
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