Abstract
A machine reading a human written English digit is a subject of research for more than three decades. As every person will have their own writing style, it is very difficult to recognize the correct handwritten characters and digits. Handwriting recognition systems are developed to achieve the accuracy and reliable performance. But, the recognition of character and digits consists of image and pattern recognition which makes it the most difficult and challenging area. In this paper the experimentation done on the classification of different hand written english numbers with preprocessing of the image obtained from which digits are to be extracted. This paper uses five Machine Learning Algorithms namely Random Forest Classifier, Linear SVC, K Nearest Neighbors, Naive Bayes and Gradient Boosting Classifier. The best algorithm thus obtained by comparing the different metrics of the algorithms considered to recognize the digits from preprocessed image.
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Latha Kalyampudi, P.S., Srinivasa Rao, P., Swapna, D. (2020). An Efficient Digit Recognition System with an Improved Preprocessing Technique. In: Gunjan, V., Garcia Diaz, V., Cardona, M., Solanki, V., Sunitha, K. (eds) ICICCT 2019 – System Reliability, Quality Control, Safety, Maintenance and Management. ICICCT 2019. Springer, Singapore. https://doi.org/10.1007/978-981-13-8461-5_34
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DOI: https://doi.org/10.1007/978-981-13-8461-5_34
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