Abstract
Scene text analysis is a field of research that poses challenges to researchers owing to the background complexities, image quality, text orientation, text size, etc. The problem gets more complex when the image contains multi-lingual texts. Most scene text detection techniques approach the problem as either a feature-based or deep learning-based problem. In this work, an end-to-end system is proposed for scene text detection, localization and language identification to combine feature-based and deep learning-based approaches. The model uses Maximally Stable Extremal Regions and Stroke Width Transform for generating text proposals, followed by proposal refinement using Generative Adversarial Network. Finally, a Convolution Neural Network based model is used for language identification of the detected scene texts. Experiments have been conducted on standard datasets like KAIST, COCO, CTW1500, CVSI and ICDAR along with an in-house multi-lingual Indic scene text dataset for which the proposed model achieves satisfactory results.
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