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ABSTRACT
In today’s world advancement in sophisticated scientific techniques is pushing further the limits of human outreach in various fields of technology. One such field is the field of character recognition commonly known as OCR (Optical Character Recognition). In this fast paced world there is an immense urge for the digitization of printed documents and documentation of information directly in digital form. And there is still some gap in this area even today. OCR techniques and their continuous improvisation from time to time is trying to fill this gap. This project is about devising an algorithm for recognition of hand written characters also known as HCR (Handwritten Character Recognition) leaving aside types of OCR that deals with recognition of computer or typewriter printed characters. A novel technique is proposed for recognition English language characters using Artificial Neural Network including the schemes of feature extraction of the characters and implemented. The persistency in recognition of characters by the Artificial neural network was found to be more than 90% of times.
INTRODUCTION
This project, ‘Handwritten Character Recognition’ is a software algorithm project to recognize any hand written character efficiently on computer with input in an image format . Character recognition, usually abbreviated to optical character recognition or shortened OCR, is the mechanical or electronic translation of images of handwritten, typewritten or printed text (usually captured by a scanner) into machine-editable text. It is a field of research in pattern recognition, artificial intelligence and machine vision. Though academic research in the field continues, the focus on character recognition has shifted to implementation of proven techniques. Optical character recognition is a scheme which enables a computer to learn, understand, improvise and interpret the written or printed character in their own language, but present correspondingly as specified by the user. Optical Character Recognition uses the image processing technique to identify any character computer/typewriter printed or hand written. A lot of work has been done in this field. But a continuous improvisation of OCR techniques is being done
based on the fact that algorithm must have higher accuracy of recognition, higher persistency in number of times of correct prediction and increased execution time. The idea is to device efficient algorithms which get input in digital image format. After that it processes the image for better comparison. Then after the processed image is compared with already available set of font images. The last step gives a
prediction of the character in percentage accuracy
Abstract:
The project aims to develop a comprehensive Handwritten Digit Recognition system leveraging Python and web technologies. Handwritten digit recognition is a critical task with applications in various fields, such as document processing, signature verification, and digitizing historical documents. The proposed system employs machine learning algorithms to accurately recognize handwritten digits and presents a user-friendly web interface for seamless interaction.
Existing System:
The current landscape of handwritten digit recognition relies on traditional methods that often lack the accuracy required for real-world applications. Conventional techniques include image processing and feature extraction, but these approaches may struggle with diverse handwriting styles and variations.
Proposed System:
The proposed system integrates advanced machine learning algorithms, particularly convolutional neural networks (CNNs), to enhance handwritten digit recognition accuracy. The system incorporates a web-based interface for user interaction, making it accessible and user-friendly. The utilization of a CNN model allows the system to learn intricate patterns and variations in handwritten digits, thus improving overall recognition performance.
Modules Explanation:
- Data Preprocessing:
- Input images undergo preprocessing to enhance features and reduce noise, ensuring optimal performance during training and testing phases.
- Convolutional Neural Network (CNN):
- The heart of the system, the CNN module, is responsible for learning and recognizing patterns in handwritten digits. It comprises layers such as convolutional layers, pooling layers, and fully connected layers for effective feature extraction and classification.
- Web Interface:
- The web interface module facilitates user interaction, allowing users to upload handwritten digit images, view recognition results, and obtain feedback.
System Requirements:
- Hardware:
- A computer with sufficient processing power for training and testing the machine learning model.
- Webcam or image input device for real-time digit recognition.
- Software:
- Python for implementing machine learning algorithms.
- Web development frameworks (e.g., Flask or Django) for creating the web interface.
- TensorFlow or PyTorch for building and training the CNN model.
Algorithms:
- Convolutional Neural Network (CNN):
- Leverage CNN architecture for feature extraction and classification of handwritten digits.
Hardware and Software Requirements:
- Hardware:
- Minimum 4GB RAM, multi-core processor for efficient model training.
- Webcam or image input device.
- Software:
- Python 3.x
- TensorFlow or PyTorch
- Web development framework (Flask or Django)
- HTML, CSS, and JavaScript for web interface development.
Architecture:
- Input Layer: Accepts images of handwritten digits.
- Convolutional Layers: Extract features from input images.
- Pooling Layers: Reduce dimensionality and retain essential features.
- Fully Connected Layers: Classify extracted features into digit categories.
- Output Layer: Provides the final recognition result.
Technologies Used:
- Python, TensorFlow or PyTorch for machine learning.
- Web development frameworks (Flask/Django) for creating the web interface.
- HTML, CSS, JavaScript for designing an interactive and user-friendly web interface.
Web User Interface:
The system offers a responsive web interface where users can upload images containing handwritten digits. The interface provides real-time feedback by displaying the recognized digit along with a confidence score. Additionally, users can access historical recognition results and performance metrics.
In conclusion, the Handwritten Digit Recognition system combines state-of-the-art machine learning algorithms with a user-friendly web interface, providing an accurate and accessible solution for recognizing handwritten digits. The integration of modern technologies ensures robust performance, making it suitable for various applications in digit recognition and document processing.
UML DIAGRAMS
Collaboration Diagram
Architecture diagram
class diagram
sequence diagram
use case diagram
activity diagram
component diagram
Deployment Diagram
Flow chart Diagram
