Developing a hand-written character recognition system using Kohonen’s self-organization map technique is the focus of our research paper.

Handwritten Character Recognition System Using Kohonen Self-Organization Map

Introduction

In the field of artificial intelligence and machine learning, handwritten character recognition is a challenging problem that has gained significant attention in recent years. With the advancement of technology, there is a growing demand for systems that can accurately and efficiently recognize handwritten characters for various applications such as optical character recognition, signature verification, and postal automation.

One of the popular approaches for handwritten character recognition is the use of Kohonen self-organization map, which is a type of artificial neural network that is trained using unsupervised learning. In this project, we aim to develop a handwritten character recognition system using Kohonen self-organization map to improve the accuracy and efficiency of character recognition.

Problem Statement

The existing handwritten character recognition systems often face challenges in accurately recognizing characters due to variations in writing styles, sizes, and orientations. These systems rely on traditional machine learning algorithms that may not be able to effectively capture the complex patterns in handwritten characters.

Existing System

The existing handwritten character recognition systems typically use algorithms such as support vector machines, hidden Markov models, and convolutional neural networks for character recognition. While these algorithms have shown promising results in recognizing printed text, they may not perform well when it comes to recognizing handwritten characters.

Disadvantages

– The existing handwritten character recognition systems may not be able to accurately recognize characters with variations in writing styles, sizes, and orientations.
– The traditional machine learning algorithms used in the existing systems may not be effective in capturing the complex patterns in handwritten characters.
– The accuracy and efficiency of the existing systems may vary depending on the quality of the training data and the complexity of the handwritten characters.

Proposed System

In this project, we propose to develop a handwritten character recognition system using Kohonen self-organization map, which is a type of unsupervised learning algorithm that is well-suited for capturing complex patterns in handwritten characters. The Kohonen self-organization map is a neural network that is trained using competitive learning, where neurons compete to become active based on the input data.

Advantages

– The use of Kohonen self-organization map can help in accurately recognizing handwritten characters with variations in writing styles, sizes, and orientations.
– The unsupervised learning approach of Kohonen self-organization map can capture the complex patterns in handwritten characters effectively.
– The handwritten character recognition system using Kohonen self-organization map can improve the accuracy and efficiency of character recognition for various applications.

Features

– Unsupervised learning: The handwritten character recognition system uses Kohonen self-organization map for training the neural network without the need for labeled data.
– Competitive learning: The neurons in the Kohonen self-organization map compete to become active based on the input data, which helps in capturing the complex patterns in handwritten characters.
– Adaptive learning: The neural network adapts its weights based on the input data, which allows for continuous improvement in recognizing handwritten characters.

Conclusion

In conclusion, the development of a handwritten character recognition system using Kohonen self-organization map is a promising approach to improve the accuracy and efficiency of character recognition for various applications. By leveraging the unsupervised learning and competitive learning capabilities of Kohonen self-organization map, we aim to address the challenges faced by existing systems in recognizing handwritten characters effectively. This project has the potential to contribute to advancements in artificial intelligence and machine learning for handwritten character recognition.