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Handwriting character recognition has been a popular problem among scientists for a few decades. United States Postal Service can be given as an example for a company that uses the recognition of digits in real life environment consistently. USPS uses digit recognition system to extract digits from pay checks and fastens the process of sending and receiving checks. Handwriting character recognition problem can be divided into two categories. Online character recognition and offline character recognition. A recognition pattern mainly based on angle of the strokes of stylus is called online recognition. A system is called offline when system takes images as inputs and tries to predict characters from given images by applying machine learning methods. We have worked on offline character recognition problem in this project. Many machine learning methods have been proposed over the years for solving this problem. In this paper we implemented 6 most popular machine learning methods to solve offline handwriting character recognition problem and compare the performance results to decide which method gives best accuracy results under pre-defined conditions. We have selected 92255 images from NIST Special 19 Database and used them as input images during the training phase of the selected machine learning methods. These methods are SVM, Decision Tree, Bag of Trees, Artificial Neural Networks (ANN), Deep learning network with autoencoders and Convolutional Neural Networks (CNN). We implemented all of these methods and compare the performance of the results according to accuracy metric. The results obtained from the comparison is going to help in deciding which ML method should be used to solve Offline Handwriting Character Recognition problem.

International Conference on Advanced Technologies, Computer Engineering and Science
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