layout: farshid_default title: “Thresholding for Handwritten Segmentation” description: “Comparison of PSNR and Otsu thresholding for handwritten image segmentation.” markmap: | # Single Thresholding Comparison ## Methods

• Otsu’s Method
• PSNR-Based Thresholding
• Multi-Level Thresholding ## Proposed Method
• PSNR Quality Measurement
• Object-Background Separation ## Results
• Better PSNR Values
• Handwritten Image Optimization ## Applications
• OCR Systems
• Mobile Devices tags: [thresholding, handwritten-recognition, image-segmentation, psnr] hashtags: “#thresholding #handwritten #imagesegmentation #psnr”

Comparison_single_thresholding_method_for_handwritten_images_segmentation

https://www.pirahansiah.com/contents/publications/Papers/Comparison_single_thresholding_method_for_handwritten_images_segmentation

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Comparison Single Thresholding Method for Handwritten Images Segmentation

1. Introduction

• Objective: Propose and compare single thresholding methods for handwritten image segmentation.
• Key Concepts:
  • Thresholding separates objects from the background in images.
  • Peak Signal-to-Noise Ratio (PSNR) is used to measure image quality.
• Importance:
  • Handwritten recognition has various applications in mobile devices and OCR systems.

2. State of the Art

• Otsu’s Method:
  • An unsupervised, nonparametric method for automatic threshold selection.
  • Uses bounding boxes of fragments and calculates global thresholds by maximizing class variance.
• Thresholding Techniques:
  • Single Thresholding: Applies a single threshold value to the entire image.
  • Multi-Level Thresholding: Uses multiple threshold values for segmenting complex images.

3. Proposed Method

• PSNR-Based Thresholding:
  • Uses PSNR to determine the quality of image segmentation.
  • Measures the effectiveness of separating objects (characters) from the background in handwritten images.
• Advantages:
  • Optimized for average-quality handwritten images.
  • Improves segmentation performance compared to other methods.

4. Experimental Results

• Method Comparison:
  • The proposed PSNR-based method is compared with Otsu’s method and other techniques.
• Performance:
  • The proposed method shows better PSNR values, indicating superior image segmentation quality.
  • Optimized for real-world handwritten images where object-background separation is crucial.

5. Conclusion

• Key Findings:
  • PSNR-based single thresholding outperforms traditional methods like Otsu’s in segmenting handwritten images.
• Implications:
  • Suitable for OCR systems and mobile applications involving handwritten text recognition.