An improved method for text detection using Adam optimization algorithm

Abstract

Optical Character Recognition (OCR) is an automatic identification technique which is applied in different application areas to translate documents or images into analysable and editable data. Printed or typed characters are easy to recognize as they have well defined shape and size, but this is not true in case of handwritten text. Handwriting of every individual is different so OCR face difficulty to recognize the characters. In past, researchers have been used different Machine Learning and Artificial Intelligence tools and techniques to analyse handwritten and printed documents and also worked to create an electronic format file from them. It is difficult to reuse this information as it is very difficult to search the content from these documents by lines or words. To solve this problem, OpenCV technique is used in this research work which focuses on training and testing of neural network model to conduct Document Image Analysis. The proposed model is named as J&M model for Text Detection from Hand written images. Implementation of research work is done in Python on MNIST database of handwritten digits. From this research work, 99.5% of training accuracy and 99% of testing accuracy was achieved along with training loss of 1.5%.