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Basic test framework for the evaluation of text line segmentation and text parameter extraction.
Darko Brodić1, Dragan R Milivojević, Zoran Milivojević
1Technical Faculty Bor, V.J. 12, University of Belgrade, 19210 Bor, Serbia. medijum@verat.net
Sensors (Basel, Switzerland)
|March 9, 2012
Summary
A new framework evaluates text line segmentation algorithms for optical character recognition (OCR). This method addresses inconsistencies in handwritten document analysis, improving text recognition accuracy.
Area of Science:
- Computer Science
- Image Processing
- Artificial Intelligence
Background:
- Text line segmentation is critical for accurate optical character recognition (OCR).
- Handwritten document analysis presents unique challenges due to handwriting variability.
- Current evaluation methods for text segmentation algorithms lack standardization.
Purpose of the Study:
- To propose a standardized test framework for evaluating text feature extraction algorithms.
- To address the need for consistent measurement methods in text segmentation.
- To improve the reliability of handwritten text recognition systems.
Main Methods:
- Development of a basic test framework for algorithm evaluation.
- Inclusion of experiments on text line segmentation, skew rate, and reference text line evaluation.
- Cross-linking of results from mutually independent experiments.
Main Results:
- The proposed framework offers a consistent approach to evaluating text analysis algorithms.
- Demonstrated suitability for various scripts and languages.
- Highlighted adaptability as a key advantage.
Conclusions:
- The presented framework provides an efficient method for evaluating text analysis algorithms.
- Standardized evaluation is essential for advancing handwritten document image processing.
- The framework contributes to more reliable OCR systems.
