Efficient text localization in born-digital images by local contrast-based segmentation

Chen, Kai; Yin, Fei; Hussain, Amir; Liu, Cheng-Lin

Abstract

Text localization in born-digital images is usually performed using methods designed for scene text images. Based on the observation that text strokes in born-digital images mostly have complete contours and the pixels on the contours have high contrast compared with the adjacent non-text pixels, we propose a method to extract candidate text components using local contrast. First, the image is segmented into smooth and non-smooth regions. After removing non-text smooth regions, the remaining smooth regions are merged with non-smooth regions to form a candidate text image, which is binarized into high-value and low-value connected components (CCs). The CCs undergo CC filtering, line grouping and line classification to give the text localization result. Experimental results on the born-digital dataset of ICDAR2013 robust reading competition demonstrate the efficiency and superiority of the proposed method.

• Presentation Conference Type: Conference Paper (Published)
• Conference Name: 2015 13th International Conference on Document Analysis and Recognition (ICDAR)
• Start Date: Aug 23, 2015
• End Date: Aug 26, 2015
• Online Publication Date: Nov 23, 2015
• Publication Date: 2015
• Deposit Date: Oct 10, 2019
• Publisher: Institute of Electrical and Electronics Engineers
• Pages: 291-295
• Book Title: 2015 13th International Conference on Document Analysis and Recognition (ICDAR)
• DOI: https://doi.org/10.1109/ICDAR.2015.7333770
• Keywords: Text localization, image segmentation, local contrast, connected components grouping
• Public URL: http://researchrepository.napier.ac.uk/Output/1792897