Curved Text Line Rectification via Bresenham’s Algorithm and Generalized Additive Models
This paper presents a methodology for rectifying curved text lines, a crucial process in optical character recognition (OCR) and computer vision. Utilizing generalized additive models (GAMs), the proposed method accurately estimates text curvature and rectifies it into a straight format for improved...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-10-01
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| Series: | Signals |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2624-6120/5/4/39 |
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| Summary: | This paper presents a methodology for rectifying curved text lines, a crucial process in optical character recognition (OCR) and computer vision. Utilizing generalized additive models (GAMs), the proposed method accurately estimates text curvature and rectifies it into a straight format for improved text recognition. The process includes image binarization techniques like Otsu’s thresholding, morphological operations, curve estimation, and the Bresenham line drawing algorithm. The results show significant improvements in OCR accuracy among different challenging distortion scenarios. The implementation, written in Python, demonstrates the potential for enhancing text alignment and rectification in scanned text line images utilizing a flexible, robust, and customizable framework. |
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| ISSN: | 2624-6120 |