Text extraction and recognition from natural scene images is a challenging task due to their complex background. It has several computer vision applications like license plate recognition, content based image retrieval, digitization for visually impaired etc. In these images, dark text can be present on a bright background or vice versa and there is an imperative need to determine this polarity for the recognition process. In the present work, we have proposed to use deep learning approaches to determine text polarity. We have used Convolutional Neural Network (CNN) to classify whether a scene image contains dark text on a bright background or vice versa. CNN has been trained on image samples collected from benchmarking datasets like ICDAR, IIIT5K etc. We have also extracted CNN features by removing its final fully connected layers and trained support vector machine (SVM) classifier using these features. Our experiments have shown that this transfer learning approach has given better accuracy than original CNN and the corresponding results are reported. (C) 2020 Elsevier B.V. All rights reserved.
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