Recognition of Handwritten Digits and English Texts using MNIST and EMNIST Datasets

Abstract

Our project is meant to implement recognition to text and digit of any input image. We separate our project into two parts ie, recognition of digit and character by using both CNN and LSTM algorithms. Our approach builds upon yan Lecun datasets in digit recognition, and applies the techniques to characters’ recognition. Yan lecun datasets include MNIST which is for recognition of digits and EMNIST for recognition of alphabets. Handwritten digit recognition is the ability of a computing system to acknowledge the written inputs like characters etc from a large sort of sources like emails, papers, images, letters etc. This has been a subject of analysis for many years. A number of the analysis areas embrace bank check processing, postal address interpretation from envelopes etc. Handwritten digit recognition plays a significant role in many user authentication applications in the modern world. Character Recognition has been an active area of research in the past and due to its diverse applications it continues to be a challenging topic. The idea is to provide efficient algorithms which takes input as character and predict the output in digital format. After that it processes the image for better comparison. Then after the processed image is compared with already available set of font images. The last step gives a prediction of the character in percentage accuracy.