Affiliations: [a] Department of Information Technology, Hazara University Mansehra, Pakistan
| [b] Department of Information Technology, University of Technology Sydney, Sydney, Australia
Correspondence:
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Corresponding author. Syed Hamad Shirazi, Department of Information Technology, Hazara University Mansehra, Pakistan. E-mail: [email protected].
Abstract: Automatic logo detection and recognition is significantly growing due to the increasing requirements of intelligent documents analysis and retrieval. The main problem to logo detection is intra-class variation, which is generated by the variation in image quality and degradation. The problem of misclassification also occurs while having tiny logo in large image with other objects. To address this problem, Patch-CNN is proposed for logo recognition which uses small patches of logos for training to solve the problem of misclassification. The classification is accomplished by dividing the logo images into small patches and threshold is applied to drop no logo area according to ground truth. The architectures of AlexNet and ResNet are also used for logo detection. We propose a segmentation free architecture for the logo detection and recognition. In literature, the concept of region proposal generation is used to solve logo detection, but these techniques suffer in case of tiny logos. Proposed CNN is especially designed for extracting the detailed features from logo patches. So far, the technique has attained accuracy equals to 0.9901 with acceptable training and testing loss on the dataset used in this work.
Keywords: Logo detection, logo recognition, deep learning, AlexNet, ResNet, CNN
