A new method for expiration code detection and recognition using gabor features based collaborative representation. Issue 4 (October 2015)

• Record Type: Journal Article
• Authors: Zaafouri, Ahmed; Sayadi, Mounir; Fnaiech, Farhat; Jarrah, Omar al.; Wei, Wu
• Is Part Of: Advanced engineering informatics. Volume 29:Issue 4(2015:Oct.)
• Page Start: 1072
• Page End: 1082
• Publication Date: 2015-10
• Subjects: Text detection -- Optical character recognition -- Gabor features -- Sparse representation -- Collaborative representation -- Principal component analysis; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2015.07.009
• Languages: English
• ISSNs: 1474-0346

Abstract

Abstract: Text in images and video contains important information for visual content understanding, indexing, and recognizing. Extraction of this information involves preprocessing, localization and extraction of the text from a given image. In this paper, we propose a novel expiration code detection and recognition algorithm by using Gabor features and collaborative representation based classification. The proposed system consists of four steps: expiration code location, character isolation, Gabor features extraction and characters recognition. For expiration code detection, the Gabor energy (GE) and the maximum energy difference (MED) are extracted. The performance of the recognition algorithm is tested over three Gabor features: GE, magnitude response (MR) and imaginary response (IR). The Gabor features are classified based on collaborative representation based classifier (GCRC). To encompass all frequencies and orientations, downsampling and principal component analysis (PCA) are applied in order to reduce the features space dimensionality. The effectiveness of the proposed localization algorithm is highlighted and compared with other existing methods. Extensive testing shows that the suggested detection scheme outperforms existing methods in terms of detection rate for large image database. Also, GCRC show very competitive results compared with Gabor feature sparse representation based classification (GSRC). Also, the proposed system outperforms the nearest neighbor (NN) classifier and the collaborative representation based classification (CRC).