# Character Recognition
## Feature Extraction
- GLCM (Texture)
- EDMS (Edge Directions)
## Proposed Method
- Hybrid GLCM + EDMS
- Improved Accuracy
## Classifiers
- Neural Networks
- Bayesian Networks
- Decision Trees
## Results
- Binary Character Images
- Outperforms Individual Methods
Character_Recognition_Based_on_Global_Feature
https://www.pirahansiah.com/contents/publications/Papers/Character_Recognition_Based_on_Global_Feature
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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.