New approach based on texture and geometric features for text detection

Authors:
Hinde Anoual;Sanaa El Fkihi;Abdelilah Jilbab;Driss Aboutajdine
Affiliations:
LRIT, unité associée au CNRST, FSR, Mohammed V University Agdal, Morocco;LRIT, unité associée au CNRST, FSR, Mohammed V University Agdal, Morocco and ENSIAS, Mohammed V University Soussi, Rabat, Morocco;LRIT, unité associée au CNRST, FSR, Mohammed V University Agdal, Morocco and ENSET, Madinat AL Irfane, Rabat-Instituts, Rabat, Morocco;LRIT, unité associée au CNRST, FSR, Mohammed V University Agdal, Morocco
Venue:
ICISP'10 Proceedings of the 4th international conference on Image and signal processing
Year:
2010

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Abstract

Due to the huge amount of data carried by images, it is very important to detect and identify the text region as accurately as possible before performing any character recognition. In this paper we describe a text detection algorithm in complex background. It is based on texture and connected components analysis. First we abstract texture regions which usually contain text. Second, we segment the texture regions into suitable objects; the image is segmented into three classes. Finally, we analyze all connected components present in each binary image according to the three classes with the aim to remove non-text regions. Experiments on a benchmark database show the advantages of the new proposed method compared to another one. Especially, our method is insensitive to complex background, font size and color; and offers high precision (83%) and recall(73%) as well.