Deep Learning Method for Handwriting Recognition

Abstract

The advancement of technology nowadays resulted into documents, such as forms and petitions, being filled out in computer and digital environment. Yet in some cases, documents are still preserved in traditional style, on print. Due to its distinct proportions, however, its storage, sharing and filing has become a complication. The relocation of these written documents to digital environment is therefore of great significance. In this view, this study aims to explore methodologies of digitizing handwritten documents. In this study, the documents converted to image format were pre-processed using image processing methods. These operations include dividing lines of the document into image format, dividing into words which then divided into characters, and finally, a classification operation on the characters. As classification phase, one of the deep learning methods is the Convolution Neural Network method is used in image recognition. The model was trained using the EMNIST dataset, and in the character, dataset created from the documents at hand. The dataset created had a success rate of 87.81%. Characters classified as finishers are sequentially combined and the document is transferred to the computer afterwards.