The task of handwritten digit recognition, using a classifier, has great importance and use such as – online handwriting recognition on computer tablets , recognize zip codes on mail for postal mail sorting, processing bank check amounts, numeric entries in forms filled up by hand (for example ‐ tax forms) and so on.
Detection of handwritten numbers, including accuracy in these areas, has reached human perfection using deep convolutional neural networks (CNNs). Hence, we are using the Convolutional Neural Network (CNN) and MNIST dataset for more accurate results.
The purpose of the handwriting recognition system is to convert handwritten letters into machine-readable formats. Thus, the heart of our project lies within the ability to develop an efficient algorithm that can recognize the handwritten digits which are scanned and sent as input by the user.
following are also one of the provlem this model solves:

• Medical diagnosis: Handwritten digit recognition can be used in medical diagnosis to automatically recognize handwritten numerical values on medical forms and records.

• Transportation: Handwritten digit recognition can be used in transportation applications such as license plate recognition, enabling automatic recognition of license plates for toll collection, parking enforcement, and other applications.