Title :
Text detection in born-digital images using multiple layer images
Author :
Chao Zeng ; Wenjing Jia ; Xiangjian He
Author_Institution :
Res. Centre for Innovation in IT Services & Applic. (iNEXT), Univ. of Technol., Sydney, NSW, Australia
Abstract :
In this paper, a new framework for detecting text from webpage and email images is presented. The original image is split into multiple layer images based on the maximum gradient difference (MGD) values to detect text with both strong and weak contrasts. Connected component processing and text detection are performed in each layer image. A novel texture descriptor named T-LBP, is proposed to further filter out non-text candidates with a trained SVM classifier. The ICDAR 2011 born-digital image dataset is used to evaluate and demonstrate the performance of the proposed method. Following the same performance evaluation criteria, the proposed method outperforms the winner algorithm of the ICDAR 2011 Robust Reading Competition Challenge 1.
Keywords :
gradient methods; image texture; object detection; support vector machines; visual databases; ICDAR 2011 Robust Reading Competition Challenge 1; MGD values; SVM classifier; T-LBP; Web page; born-digital image dataset; email images; maximum gradient difference values; multiple layer images; nontext candidates; performance evaluation criteria; strong contrasts; text detection; texture descriptor; weak contrasts; winner algorithm; Conferences; Electronic mail; Image edge detection; Pattern recognition; Robustness; Support vector machines; Vectors; Multiple layer image; T-LBP; maximum gradient difference; text detection;
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on
Conference_Location :
Vancouver, BC
DOI :
10.1109/ICASSP.2013.6637993