A hierarchical and scalable model for contemporary document image segmentation

• Published in: Pattern analysis and applications : PAA Vol. 16; no. 4; pp. 679 - 693
• Main Authors: Ouji, Asma, Leydier, Yann, LeBourgeois, Frank
• Format: Journal Article
• Language: English
• Published: London Springer London 01.11.2013; Springer; Springer Verlag
• Subjects: Applied sciences; Artificial intelligence; Computer Science; Computer science; control theory; systems; Exact sciences and technology; Industrial and Commercial Application; Pattern Recognition; Pattern recognition. Digital image processing. Computational geometry; Advertisement classification; Document image; Text detection; Color segmentation; Noisy image; Image recognition; Scalability; Image processing; Modeling; Adaptive method; Information loss; Document processing; Signal quantization; Robustness; Advertising; Document analysis; Computer vision; Digitizing; Text; Character recognition; Color image; Image segmentation; Optical character recognition; Document structure; Hierarchical system
• ISSN: 1433-7541; 1433-755X
• DOI: 10.1007/s10044-012-0282-x

In this paper, we introduce a novel color segmentation approach robust against digitization noise and adapted to contemporary document images. This system is scalable, hierarchical, versatile and completely automated, i.e. user independent. It proposes an adaptive binarization/quantization without any penalizing information loss. This model may be used for many purposes. For instance, we rely on it to carry out the first steps leading to advertisement recognition in document images. Furthermore, the color segmentation output is used to localize text areas and enhance optical character recognition (OCR) performances. We held tests on a variety of magazine images to point up our contribution to the well-known OCR product Abby FinerReader. We also get promising results with our ad detection system on a large set of complex layout testing images.