A Machine Learning Framework for Recognizing Handwritten Digits Using Convexity Based Feature Vector Encoding
Handwritten digit recognition has always been an active topic in OCR applications as it stems out of pattern recognition research. In our day to day life, character image recognition is required while processing postal mail, bank cheque handwritten application form, license plate image, and other document images. In recent years, handwritten digit recognition has been playing a key role even for user authentication applications. In this proposed work, we develop a gradient descent ANN model using novel and unique geometric feature extraction technique for handwritten digit recognition system which can be further extended to identify any alphanumeric character images. We have extracted geometric features of handwritten digit based on computational geometric method and applied artificial neural network (ANN) technique for classification of handwritten digits through machine learning approach. The characteristics of extracted feature for a digit class are found to be distinct despite wide variations within the class and thereby lead to reasonably good recognition rate even with small trainee samples.