Handwriting Recognition using Deep Learning with Effective Data Augmentation Techniques

Machine learning techniques have been successfully used in deciphering handwritten text. Deep learning has made further improvements in this regard. However, they require substantial amounts of training data. This research aims to improve the effectiveness of classification accuracy in the presence of limited training data on handwriting recognition. The main focus thus involves enabling deep models to converge during training on smaller datasets using data augmentation. This will allow for broader use of these systems across more regions, greater accessibility, and future related systems to be less reliant on the amount of data available. Therefore, the proposed research includes an image processing and machine learning approach to handwriting recognition while generating more sample data in various ways. Applying random cropping as an augmentation technique resulted in higher accuracy than several other augmentation techniques examined in this paper. Some of these techniques performed worse than on unaugmented data.