CSEIT206396 | Accepted : 18 June 2020 | Published : 25 June 2020 | May-June-2020 [ 6 (3) : 896-902 ] International Journal of Scientific Research in Computer Science, Engineering and Information Technology © 2020 IJSRCSEIT | Volume 6 | Issue 3 | ISSN : 2456-3307 DOI : https://doi.org/10.32628/CSEIT206396 896 Handwritten Digit Recognition using Image Preprocessing and CNN Anshul Dubey, Ashley Lazarus, Dharmendra Mangal Computer Science Department, MediCaps University, Indore, Madhya Pradesh, India ABSTRACT Handwritten digit recognition, is a technique of identifying and enlisting the recognized digit, that uses neural networks, deep learning and machine learning. The applications and demand of handwritten digit recognition systems such as zip code recognition, car number plate recognition, robotics, banks, mobile applications and numerous more, are soaring every day. It can be done through numerous approaches, but convolutional neural network is considered one of the best methods. The special neural network uses multilayer architecture for identification and classification. Although the accuracy factor can be increased, based on image preprocessing, in this paper we discuss how the accuracy of the system can be increased for better handwritten digit recognition, using convolutional neural networks, image preprocessing; binarization, resizing, rotation. The accuracy rate obtained is 99.33%. Keywords : Digit Recognition, Image Processing, CNN. I. INTRODUCTION Digit recognition is an application of a computer system which makes it able to recognize the handwritten inputs like digits from various kinds of sources like images, papers, emails, letters and more. This ability of a computer is very important as it can reduce manpower in areas like postal address interpretation, bank check processing, signature verification, etc. This topic has been for a lot of years a big topic of research. A lot of classification techniques using Machine Learning have been developed and used for this like K-Nearest Neighbors, SVM Classifier, Random Forest Classifier, etc. but these methods although have enough accuracy, are not enough for the real-world applications. One example of this is, if you send a letter with postal code as “452311” and the system detects and recognizes it as “452811” then it will not be able to deliver the letter due to incorrect postal code [1]. Thus, accuracy in these applications is very critical but these techniques do not provide the required accuracy due to very little knowledge about the topology of a task. Here comes the use of Deep Learning. In the past decade, deep learning has become the hot tool for Image Processing, object detection, handwritten digit and character recognition etc. A lot of machine learning tools have been developed like scikit-learn, SciPy-image and pybrains, Keras, Theano, TensorFlow by Google, TFLearn etc. for Deep Learning. These tools make the applications robust and therefore more accurate, also the Artificial Neural Networks can almost mimic the human brain and are a key ingredient in image processing field. We applied CNN model, using Keras library in python, for handwritten digit recognition, with improves image preprocessing. Implementing a better and accurate approach to perceive and foresee manually written digits from 0 to 9 from images [2]. Previously many problems have been faced in this research area one of them which