An approach to a comprehensive test framework for analysis and evaluation of text line segmentation algorithms

Area of Science:

• Computer Science
• Image Processing
• Pattern Recognition

Background:

• Text line segmentation is vital for accurate optical character recognition (OCR).
• Existing evaluation methods for text line segmentation algorithms often rely on text databases, leading to potential mismatches and unreliable testing.
• A robust and comprehensive framework is needed for validating text line segmentation algorithms.

Purpose of the Study:

• To introduce a novel, comprehensive testing framework for evaluating text line segmentation algorithms.
• To address the limitations of current evaluation methods by incorporating diverse data types and cross-linked test results.
• To propose new procedures for assessing algorithm efficiency based on error classification.

Main Methods:

• Development of a testing framework utilizing both synthetic multi-like text samples and real handwritten text.
• Cross-linking of results from mutually independent tests for a holistic evaluation.
• Proposal of two distinct procedures for evaluating algorithm efficiency: one based on segmentation line error description and another using signal detection theory.

Main Results:

• The proposed framework provides a reliable method for evaluating text line segmentation algorithms.
• The framework is adaptable for different scripts and languages.
• Two novel procedures for algorithm efficiency evaluation were introduced, offering complementary insights.

Conclusions:

• The developed testing framework offers a significant advancement in the reliable evaluation of text line segmentation algorithms.
• The proposed efficiency evaluation procedures enhance the assessment of algorithm performance.
• This approach facilitates more accurate and dependable OCR systems.