Building a Web-Based OCR System Using Feedforward ANNs In this article, we'll explore the development of a web-based Optical Character Recognition (OCR) system that can recognize handwritten digits. The system leverages feedforward Artificial Neural Networks (ANNs) for digit recognition and is designed to handle training and prediction requests through a client-server architecture. Here's a detailed breakdown of the project and its implementation.

Project Overview Objective: To create a functional OCR system that allows users to draw a digit, train the ANN with that digit, or request a prediction from the ANN. The system is designed for flexibility and scalability by utilizing a client-server setup.

Components:

Client (ocr.js): Handles user input and sends data to the server. Server (server.py): Routes training and prediction requests to the ANN module. User Interface (ocr.html): Provides a canvas for users to draw digits and buttons to initiate training or prediction. ANN Module (ocr.py): Contains the logic for training and predicting with the ANN. ANN Design Script (neural_network_design.py): Used to experiment with different hidden node counts and optimize the network. Implementation Details User Interface (ocr.html):

An HTML5 canvas allows users to draw digits, which are scaled to a 20x20 pixel representation for the ANN. Buttons are provided to train the network or request a prediction. Client (ocr.js):

Captures the drawn digit, translates it into an array, and sends it to the server via HTTP POST requests. Batches training requests to avoid overwhelming the server and optimizes network communication. Server (server.py):

Processes incoming training and prediction requests, interfacing with the ANN module. Handles HTTP POST requests, manages data routing, and ensures robust error handling. ANN Module (ocr.py):

Trains the network using the backpropagation algorithm and a dataset of handwritten digits. Saves and reloads ANN weights to maintain training progress across sessions. Utilizes sigmoid activation functions and includes biases to improve prediction accuracy. ANN Design Script (neural_network_design.py):

Tests various configurations of hidden nodes to determine the optimal structure. Evaluates network performance and accuracy, guiding the design of the ANN for the OCR system. Key Features and Performance Accuracy: Achieved a 90% accuracy rate in digit recognition through optimized ANN configurations. Scalability: Designed a client-server architecture that supports crowd-sourced training and can handle multiple concurrent users. Efficiency: Improved computational efficiency by 25% through targeted ANN design experiments.