A Vision Approach for Expiry Date Recognition using Stretched Gabor Features

Product-expiry date represent important information for products consumption. They must contain clear information in the label. The expiry date information stamped on the cover of product faced some challenges due to their writing in pencil and distorted characters. In this paper, an automated vision approach for recognizing expiry date numerals of industrial product is presented. The system consists of four stages namely, numeral string pre-processing, numerals string segmentation, features extraction and numeral recognition. In pre-processing module, we convert the image to binary image based on threshold. A vertical projection process is adopted to isolate numerals, in the segmentation module. In the features extraction module, Fourier Magnitude (FM), Local Energy (LE) and Complex Moments (CM) derived from Stretched Gabor (S-Gabor) filters outputs are extracted at various filter orientations. Also, the mean and the variance of each feature map are extracted. The recognition process is achieved by classifying the extracted features, which represent the numeral image, with trained Multilayer Neural Network (MNN) using k-fold cross validation procedure. Through experiments, we demonstrate the richness of the S-Gabor features of information is highlighted. Consequently, the set of features shows its usefulness for