Realtime Handwritten Digit Recognition Using Keras Sequential Model and Pygame

EasyChair Preprint 3364

Abstract

Handwritten recognition has received the greater attention in deep learning research community due to its vast applications and ambiguity in learning methods. CNN in deep learning is now becoming one of the most appealing approaches and has been a crucial factor in the variety of recent success and challenging machine learning applications such as object detection. This research paper is about the extended application of handwritten digit recognition i.e. Realtime detection of the handwritten digits. CNN is used as the Model for the classification of the image and more specifically Keras Sequential Model is used as a classifier. The interface is created by pygame. The layout of the interface is kept simple and is divided into two frames one for input and other for output. The image pre-processing is the most important step which has done with the help of OpenCV and Scipy. MNIST is the dataset used for training and testing. Handwritten Digit Recognition has various real-life time uses. It is used in the detection of vehicle number, banks for reading cheques, post offices for arranging letter, and many other tasks.

Keyphrases: Convolutional Neural Network (CNN), Handwritten Digit Recognition, deep learning, sequential model

@booklet{EasyChair:3364,
  author    = {K. Senthil Kumar and Suman Kumar and Aabhash Tiwari},
  title     = {Realtime Handwritten Digit Recognition Using Keras Sequential Model and Pygame},
  howpublished = {EasyChair Preprint 3364},
  year      = {EasyChair, 2020}}