Character recognition in natural scene images using rank-1 tensor decomposition
Muhammad Sajid Ali, Hassan Foroosh · 2016
Many approaches to solve the problem of scene character recognition utilize local features such as histograms of oriented gradients (HoG), SIFT, Shape Contexts (SC), Geometric Blur (GB), etc. An issue associated with these methods is the ad hoc rasterization of the local features into a single vector which perturbs the global spatial correlations that carry crucial information for recognition. To address this issue, we propose a novel holistic solution by incorporating tensor decomposition to get image features and utilizing image-to-class distance metric learning (I2CDML) for classification. For each training image, we first form a 3-mode tensor by rotating it through a sequence of angles. Then we perform rank-1 decomposition on the tensor to get the descriptor for each image. Utilizing the I2CDML framework, we then learn metrics for each class that are finally used to classify test images. We report results on popular natural scene character datasets, namely Chars74K-Font, Chars74K-Image, and ICDAR2003. We achieve results better than several baseline methods based on local features (e.g. HoG) and show that leave-random-one-out-cross validation yield even better recognition performance.