# LPR with Entropy Multi-threshold
## Entropy-Based Thresholding
- Cross Entropy Maximization
- Pun/Kapur Methods
## Proposed Method
- Multi-threshold Selection
- Histogram Integration
## Recognition Process
- Image Segmentation
- Character Segmentation
- Recognition
## Results
- vs Single Threshold
- Varying Conditions
License_Plate_Recognition_with_Multi-Threshold_Based_on_Entropy
https://www.pirahansiah.com/contents/publications/Papers/License_Plate_Recognition_with_Multi-Threshold_Based_on_Entropy
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License Plate Recognition with Multi-threshold based on Entropy
1. Introduction
- Objective: Propose a multi-thresholding method for license plate recognition.
- Importance of Thresholding:
- Simplifies image segmentation
- Ensures robustness and accuracy in recognizing license plate characters.
- Challenges:
- Selecting the correct threshold values for better segmentation results.
2. Entropy-based Thresholding
- Method:
- Based on maximizing the cross entropy between the original image and the segmented image.
- Entropy is treated as a probability distribution of the image histogram.
- Historical Background:
- Originally proposed by Pun and later improved by Kapur for image segmentation.
- Entropy-based thresholding is widely used for bi-level and multi-level thresholding.
3. Proposed Method
- Multi-thresholding Based on Maximum Entropy:
- Selects several threshold values by maximizing the entropy.
- Integrates partial ranges of the image histogram to achieve better segmentation.
- Comparison with Other Methods:
- Compared to single-thresholding techniques based on maximum entropy.
- Evaluates how multi-thresholding enhances the recognition accuracy.
4. License Plate Recognition Process
- Steps:
- Image Segmentation: Separates the license plate region using multi-thresholding.
- Character Segmentation: Applies the selected thresholds to segment individual characters.
- Recognition: Recognizes the segmented characters to produce the license plate number.
5. Results and Discussion
- Performance:
- The proposed multi-threshold method outperforms single-thresholding techniques.
- Shows improved segmentation results for varying lighting and environmental conditions.
6. Conclusion
- Key Findings:
- Multi-thresholding based on entropy enhances the accuracy of license plate recognition.
- Implications:
- Can be applied in real-world license plate recognition systems with better robustness.