Handwritten Prescription Letter Recognition Web Application

The Handwritten Letter Recognition Web Application is a Flask-based system that accurately predicts handwritten letters from uploaded images. It utilizes TensorFlow, OpenCV, and a pre-trained deep neural network model to provide interactive and visual recognition results.

Category:

Sub-category:

Computer Vision

Text Recognition

Overview:

This project focuses on the development of a web application for handwritten letter recognition. The application allows users to upload images of handwritten letters and provides predictions of the corresponding letters. The application utilizes Flask, OpenCV, and TensorFlow to build an interactive and accurate letter recognition system.

Description:

The Handwritten Letter Recognition Web Application is designed to recognize and predict letters from images of handwritten text. The application provides users with an intuitive interface to upload images and obtain predictions of the corresponding letters. The underlying model is trained using TensorFlow and has been optimized for accurate letter recognition.

The web application is built using Flask, a lightweight and flexible web framework. It provides routes for the main page, prediction, and result display. The main page allows users to upload an image file containing handwritten letters. The application checks the file extension and validates that it is an allowed image format (e.g., PNG, JPG, JPEG).

Once a valid image file is uploaded, the application preprocesses the image by converting it to grayscale and applying thresholding techniques to enhance the letter features. It then segments the image into individual letters using contour detection. Each letter image is resized and processed to match the input requirements of the trained model. The application utilizes a pre-trained model. This model is a deep neural network trained on a large dataset of handwritten letters. It leverages convolutional neural network (CNN) architecture for accurate letter recognition.

After processing the individual letter images and making predictions, the application displays the original uploaded image along with the predicted letters for each segmented letter. This provides the user with an interactive and visual representation of the recognition results.