Script and language identification in noisy and degraded document images.

Area of Science:

• Computer Vision
• Document Image Analysis
• Natural Language Processing

Background:

• Accurate identification of scripts and languages is crucial for digitizing and processing historical documents.
• Existing methods often struggle with noisy and degraded document images, limiting their applicability.

Purpose of the Study:

• To develop a robust technique for identifying scripts and languages in challenging document image conditions.
• To create a document vectorization method tolerant to variations in fonts, styles, noise, and degradation.

Main Methods:

• Document vectorization using vertical component cuts and character extremum points.
• Creation of script/language templates through a training process.
• Identification based on the distance between document vectors and pre-constructed templates.

Main Results:

• The proposed technique accurately identifies scripts and languages in noisy and degraded document images.
• The method demonstrates tolerance to various forms of document degradation and variations.
• Experimental results confirm the accuracy and robustness of the identification technique.

Conclusions:

• The developed document vectorization technique offers a reliable solution for script and language identification.
• The method is easily extensible to new scripts and languages.
• This approach enhances the accessibility and processing of degraded historical documents.