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%.

Category:

Sub-category:

Deep Learning

Keras

Overview:

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.

Description:

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.