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README.md

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-title: Hand Written Digit Recognition
+title: Hand Written Text Digit Recognition
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 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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+# Task : Handwritten Text Digit Recognition
+
+## Problem Statement:
+
+Handwritten digit recognition is a fundamental task in machine learning and computer vision that involves identifying and classifying individual digits (0-9) that have been handwritten. This task plays a crucial role in various applications across different domains.
+This project aims to develop a handwritten digit recognition system using deep learning techniques. It involves building and training models that can accurately classify handwritten digits.
+
+## Importance and Use Cases:
+
+1. Finance: Automatic check reading, where handwritten numbers on checks are recognized to process financial transactions efficiently.
+
+2. Postal Services: ZIP code recognition, aiding postal services in sorting and delivering mail accurately.
+
+3. Data Entry and Forms: Digit extraction in forms, simplifying data entry processes and reducing errors in handwritten information.
+
+4. Educational Tools: Handwritten digit recognition is also used in educational tools to provide interactive learning experiences, such as math and number games.
+
+5. OCR Systems: It is a crucial component of Optical Character Recognition (OCR) systems used for digitizing printed and handwritten text.
+
+## Challenges:
+
+Handwritten digit recognition presents several challenges, including variations in writing styles, different writing tools, noise in scanned images, and variations in digit orientation and size.
+
+
+## Overview
+
+This project aims to implement a Handwritten Digit Recognition system. We have trained a machine learning model and loaded the model weights (model_weights.pth). By running the app.py script, you can interact with the application.
+
+## Usage Instructions
+
+Follow these steps to use the application:
+
+1. **Installation**: Ensure you have installed all the required libraries and packages.
+
+2. **Model Setup**: We've trained a model and saved its weights in model_weights.pth. Make sure you have this file in your project directory.
+
+3. **Run the App**: Execute the app.py script to launch the interactive application.
+
+4. **Select Colors**: Use the application to choose the background color and stroke color. You can also load an input image or draw a digit.
+
+5. **Digit Prediction**: The model will predict the drawn digit or the digit in the loaded image.
+
+Enjoy using the Handwritten Digit Recognition app!
+
+
+