Abstract
Detection and language identification of texts in an unconstrained scene image are quintessential processes in the multimedia information retrieval domain. Over the years, various approaches have investigated them by considering detection and language identification as separate problem statements. To the best of our knowledge, scene text datasets with minority Indic languages are not yet available. To this end, we created a scene image dataset called EMBiL containing a combination of English and Manipuri text. It contains 720 scene images with a total of over 28500 text instances. The Manipuri language is one of the official languages of India. To benchmark the performance of EMBiL, we proposed a single-stage simultaneous detection and language identification network called SceneTextYOLO-Net based on YOLOv5. We specifically included the shallow layer characteristics and applied a multi-scale detection head to improve small target text detection. We also inserted an attention mechanism between the neck and head structures to concentrate on the image’s essential regions. We performed extensive experiments on the proposed dataset using various state-of-the-art techniques. Furthermore, we performed experimental analysis on ICDAR2015 using SceneTextYOLO-Net and state-of-the-art methods. EMBiL is available at: https://github.com/Naosekpam/EMBiL-Dataset.
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Naosekpam, V., Islam, M., Chourasia, A., Sahu, N. (2023). EMBiL: An English-Manipuri Bi-lingual Benchmark for Scene Text Detection and Language Identification. In: Tsapatsoulis, N., et al. Computer Analysis of Images and Patterns. CAIP 2023. Lecture Notes in Computer Science, vol 14184. Springer, Cham. https://doi.org/10.1007/978-3-031-44237-7_7
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