---
layout: farshid_default
title: "Character Recognition via Global Features"
permalink: /notes/pubs/papers/global-feature-recognition/
description: "Hybrid GLCM and EDMS method for character recognition in OCR applications."
---

Hybrid GLCM and EDMS method for character recognition in OCR applications.

Character_Recognition_Based_on_Global_Feature

https://www.pirahansiah.com/notes/pubs/papers/Character_Recognition_Based_on_Global_Feature

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[PDF Download My Conference Paper]( https://doi.org/10.1109/ICEEI.2011.6021649 )

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# Character Recognition Based on Global Feature Extraction

## 1. Introduction
   - **Objective**: Propose a combination of two feature extraction techniques for character recognition.
   - **Key Techniques**:
     - Gray Level Co-occurrence Matrix (GLCM)
     - Edge Direction Matrix (EDMS)
   - **Challenges**:
     - Selecting the best feature extraction technique for various character recognition tasks.

## 2. Feature Extraction Techniques
   - **Gray Level Co-occurrence Matrix (GLCM)**:
     - Focuses on texture and pixel relationships in the image.
     - Extracts texture features for character recognition.
   - **Edge Direction Matrix (EDMS)**:
     - Emphasizes edges and directions in character images.
     - Extracts shape features.

## 3. Proposed Method
   - **Combination of GLCM and EDMS**:
     - A hybrid approach that leverages both texture and edge features.
     - Aims to improve accuracy over using either GLCM or EDMS alone.

## 4. Classification Techniques
   - **Classifiers Used**:
     - Neural Networks (NN)
     - Bayesian Networks (BN)
     - Decision Tree Classifiers
   - **Objective**: Find the best classifier to complement the hybrid feature extraction method.

## 5. Experimental Results
   - **Datasets**:
     - Binary character images of different sizes used for testing.
   - **Performance**:
     - The hybrid method (GLCM + EDMS) outperforms individual feature extraction techniques.
     - Results show improved accuracy in character recognition tasks.

## 6. Conclusion
   - **Key Findings**:
     - The combination of GLCM and EDMS provides better recognition performance than using either technique alone.
   - **Implications**:
     - More reliable character recognition systems for real-world applications.