top of page
2 - 3 Weeks

Average time from first call to first prototype


AI services supported on all major cloud platforms

Build This Prototype

Handwritten Digit Recognition using Convolutional Neural Networks(CNN)

This project employs Keras to develop a deep learning model that accurately recognizes handwritten digits using the MNIST dataset, with accuracy typically above 98%.



Deep Learning



This project focuses on developing a handwritten digit recognition model using deep learning techniques with the Keras framework. The model is trained on a dataset of handwritten digits, such as the MNIST dataset, and utilizes convolutional neural networks (CNNs) for achieving high accuracy. By leveraging the power of Keras and CNNs, this model can accurately classify and recognize handwritten digits with remarkable precision.


The Handwritten Digit Recognition project employs deep learning to build a robust model capable of accurately identifying and classifying handwritten digits. The training dataset consists of a large number of handwritten digit images, such as the popular MNIST dataset. These images are preprocessed and augmented to ensure better training results.

Using the Keras framework, the model architecture is designed to incorporate convolutional layers, which are well-suited for detecting and recognizing patterns in images. The convolutional layers are followed by pooling layers to reduce the spatial dimensions and extract the most important features. Additional fully connected layers are then added to perform the final classification.

To evaluate the model's performance, a separate validation dataset is created by splitting the original dataset. The model's accuracy is measured on this validation dataset, providing insights into its ability to generalize to unseen handwritten digits. The achieved results demonstrate high accuracy, typically above 98%, indicating the model's effectiveness in recognizing handwritten digits.

The saved model can be utilized to predict the digits in new handwritten images. By providing a reliable and efficient solution for handwritten digit recognition, this model has diverse applications, including digit-based document processing, automated form-filling, and postal mail sorting.

  • Programming Language: Python 

  • Deep Learning Framework: Keras 

  • Library: keras

Project Demo Video Implementation


We can develop projects with similar requirements tailored to your needs, or create custom solutions specific to your requirements. This project demo showcases the underlying code-level functionality, while your final product will be more accurate when trained on real data. Additionally, we offer UI interface development for both mobile and web platforms. Contact us today to launch your first Minimum Viable Product (MVP) in the field of AI and ML.
Related Projects

Deep Learning

Brain Tumor Detection using Deep Learning

Deep Learning

Age and Gender Prediction Web Application using ResNet-50 Model

Deep Learning

Gender Detection Model using Keras

Deep Learning

Image Captioning using ResNet-50 and Flickr8k Dataset

Deep Learning

Handwritten Digit Recognition using Convolutional Neural Networks(CNN)

Project Gallery

bottom of page