Title :
Steerable Pyramid Based Complex Documents Images Segmentation
Author :
Benjelil, Mohamed ; Kanoun, Slim ; Mullot, RéMy ; Alimi, Adel M.
Author_Institution :
REGIM-ENIS, Sfax, Tunisia
Abstract :
In this paper, we propose an accurate and suitable designed system for complex documents segmentation. This system is based on steerable pyramid transform. The features extracted from pyramid sub bands serve to locate and classify regions into text and non text in some noise infected, deformed, multilingual, multi script document images. These documents contain tabular structures, logos, stamps, handwritten text blocks, photos etc. The encouraging and promising results obtained on 1000 official complex documents images data set are presented in this research paper.
Keywords :
document image processing; feature extraction; image classification; image segmentation; text analysis; transforms; document image segmentation; feature extraction; multilingual multi script document image; steerable pyramid; steerable pyramid transform; text region classification; Data mining; Feature extraction; Image analysis; Image recognition; Image retrieval; Image segmentation; Large-scale systems; Smoothing methods; Streaming media; Text analysis;
Conference_Titel :
Document Analysis and Recognition, 2009. ICDAR '09. 10th International Conference on
Conference_Location :
Barcelona
Print_ISBN :
978-1-4244-4500-4
Electronic_ISBN :
1520-5363
DOI :
10.1109/ICDAR.2009.288
