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Urdu signboard detection and recognition using deep learning

  • 1177: Advances in Deep Learning for Multimodal Fusion and Alignment
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Abstract

Signboard detection and recognition is an important task in automated context-aware marketing. Recently many scripting languages like Latin, Japanese, and Chinese have been effectively detected by several machine learning algorithms. As compared to other languages, outdoor Urdu text needs further attention in detection and recognition due to its cursive nature. Urdu detection and recognition are also difficult due to a wide variety of illuminations, low resolution, inconsistent font styles, color, and backgrounds. To overcome the deficiency of Urdu text detection from the outdoor environment, we have proposed a new Urdu-text signboard dataset with 467 ligature categories, containing a 30 + K images for recognition and 700 base images with annotation are created for detection. We also propose a methodology, that consists of 3-phases. In first phase text regions containing Urdu ligatures from shop-signboard images are detected by a faster regional convolutional neural network (FasterRCNN) using pre-trained CNNs like Alexnet and Vgg16. In the second phase detected regions from the first phase are clustered to identify unique ligatures in a dataset. Lastly in the third phase, all detected regions are recognized by 18-layer convolutional neural network trained model. The proposed system has successfully achieved the precision and recall of 87% and 96% respectively using vgg16 model for detection. For the classification of ligatures, a recognition rate of 97.50% is achieved. Recognition of ligatures was also evaluated using bilingual evaluation understudy (BLEU), and achieved an encouraging score of 0.96 on the newly developed Urdu-Signboard dataset.

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Acknowledgments

The authors would like to acknowledge Higher Education Commission (HEC) for supporting this work under their NRPU Project No. 6338. This work was also supported by FCT/MCTES through national funds and when applicable co-funded EU funds under the Project UIDB/EEA/50008/2020; and by the Brazilian National Council for Research and Development (CNPq) via Grants No. 309335/2017-5.

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Authors and Affiliations

  1. Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan

    Syed Yasser Arafat & Muhammad Javed Iqbal

  2. Department of Computer Science and Information Technology (CS&IT), Mirpur University of Science and Technology (MUST), Mirpur, 10250, AJK, Pakistan

    Syed Yasser Arafat & Nabeel Ashraf

  3. Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia

    Iftikhar Ahmad

  4. Department of Computer and Information Sciences, Northumbria University, Newcastle, NE1 8ST, UK

    Suleman Khan

  5. Federal University of Piauí (UFPI), Teresina, PI, Brazil

    Joel J. P. C. Rodrigues

  6. Instituto de Telecomunicações, Covilhã, Portugal

    Joel J. P. C. Rodrigues

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  1. Syed Yasser Arafat
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Arafat, S.Y., Ashraf, N., Iqbal, M.J. et al. Urdu signboard detection and recognition using deep learning. Multimed Tools Appl 81, 11965–11987 (2022). https://doi.org/10.1007/s11042-020-10175-2

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