Abstract
In recent years, significant advancements have been made in deep learning and the recognition of text in images of natural scenes, thanks to the advancements in machine learning and artificial intelligence. The limited availability of diverse datasets containing multiple languages and scripts often restricts the effectiveness of deep learning and text detection in the wild, particularly when it comes to Arabic language as an additional challenge. Despite notable progress, this scarcity remains a constraint. The deep learning neural network known as YOLO (You Only Look Once) has become widely popular due to its versatility in addressing a wide range of machine learning tasks, particularly in the domain of computer vision. The YOLO algorithm has gained increasing acknowledgment for its outstanding ability to tackle complex problems in conjunction with complex backgrounds of an image captured from nature, handle noisy data, and overcome various challenges encountered in real-world situations. Our experiments offer a succinct analysis of text detection algorithms that rely on convolutional neural networks (CNNs); In particular, we focus on various iterations of the YOLO models, employing same specific data augmentation techniques on both SYPHAX dataset and ICDAR MLT-2019 dataset, which comprise Arabic scripts in real natural scene images. The aim of this article is to identify the most effective YOLO algorithm for detecting text containing the Arabic scripts in the wild then to enhance this optimal model obtained in addition to explore potential research avenues that can enhance the capabilities of the most robust architecture in this field.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bai, X., Yang, M., Lyu, P., Xu, Y., Luo, J.: Integrating scene text and visual appearance for fine-grained image classification. IEEE Access 6, 66322–66335 (2018)
Abdelaziz, I., Abdou, S., Al-Barhamtoshy, H.: A large vocabulary system for Arabic online handwriting recognition. Pattern Anal. Appl. 19, 1129–1141 (2016)
Turki, H., Elleuch, M., Kherallah, M.: SYPHAX Dataset. IEEE Dataport (2023). https://doi.org/10.21227/ydqd-2443
Nayef, N., et al.: ICDAR2019 robust reading challenge on multi-lingual scene text detection and recognition—RRC-MLT-2019. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 1582–1587. IEEE (2019)
Sultana, F., Sufian, A., Dutta, P.: A review of object detection models based on convolutional neural network. Intell. Comput.: Image Proc. Based Appl., 1–16 (2020)
Turki, H., Halima, M.B., Alimi, A.M.: Text detection based on MSER and CNN features. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 1, pp. 949–954. IEEE (2017)
Amrouche, A., Bentrcia, Y., Hezil, N., Abed, A., Boubakeur, K.N., Ghribi, K.: Detection and localization of Arabic text in natural scene images. In: 2022 First International Conference on Computer Communications and Intelligent Systems (I3CIS), pp. 72–76. IEEE (2022)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)
Ravi, N., El-Sharkawy, M.: Real-time embedded implementation of improved object detector for resource-constrained devices. J. Low Power Electron. Appl. 12(2), 21 (2022)
Diwan, T., Anirudh, G., Tembhurne, J.V.: Object detection using YOLO: challenges, architectural successors, datasets and applications. Multimedia Tools Appl. 82(6), 9243–9275 (2023)
Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: Yolov4: optimal speed and accuracy of object detection. arXiv preprint: arXiv:2004.10934 (2020)
Jocher, G., Nishimura, K., Mineeva, T., Vilarino, R.: Yolov5 by ultralytics. Disponıvel em: https://github.com/ultralytics/yolov5 (2020)
Redmon, J., Farhadi, A.: Yolov3: an incremental improvement. arXiv preprint: arXiv:1804.02767 (2018)
Latha, R.S., et al.: Text detection and language identification in natural scene images using YOLOv5. In: 2023 International Conference on Computer Communication and Informatics (ICCCI), pp. 1–7. IEEE (2023)
Xu, Q., Zheng, G., Ren, W., Li, X., Yang, Z., Huang, Z.: An efficient and effective text spotter for characters in natural scene images based on an improved YOLOv5 model. In: International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2022), vol. 12588, pp. 64–68. SPIE (2023)
Luo, Y., Zhao, C., Zhang, F.: Research on scene text detection algorithm based on modified YOLOv5. In: International Conference on Mechatronics Engineering and Artificial Intelligence (MEAI 2022), vol. 12596, pp. 620–626. SPIE (2023)
Li, C., et al.: YOLOv6: a single-stage object detection framework for industrial applications. arXiv preprint: arXiv:2209.02976 (2022)
Ge, Z., Liu, S., Wang, F., Li, Z., Sun, J.: Yolox: exceeding YOLO series in 2021. arXiv preprint: arXiv:2107.08430 (2021)
Norkobil Saydirasulovich, S., Abdusalomov, A., Jamil, M.K., Nasimov, R., Kozhamzharova, D., Cho, Y.I.: A YOLOv6-based improved fire detection approach for smart city environments. Sensors 23(6), 3161 (2023)
Gupta, C., Gill, N.S., Gulia, P., Chatterjee, J.M.: A novel finetuned YOLOv6 transfer learning model for real-time object detection. J. Real-Time Image Proc. 20(3), 42 (2023)
Wang, C.Y., Bochkovskiy, A., Liao, H.Y.M.: YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7464–7475 (2023)
Negi, A., Kesarwani, Y., Saranya, P.: Text Based Traffic Signboard Detection Using YOLO v7 Architecture. In: Singh, M., Tyagi, V., Gupta, P., Flusser, J., Ören, T. (eds.) Advances in Computing and Data Sciences. Communications in Computer and Information Science, vol. 1848, pp. 1–11. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-37940-6_1
Moussaoui, H., El Akkad, N., Benslimane, M.: Arabic and Latin license plate detection and recognition based on YOLOv7 and image processing methods (2023)
Veit, A., Matera, T., Neumann, L., Matas, J., Belongie, S.: Coco-text: dataset and benchmark for text detection and recognition in natural images. arXiv preprint: arXiv:1601.07140 (2016)
Karatzas, D., et al.: ICDAR 2015 competition on robust reading. In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 1156–1160. IEEE (2015)
Tounsi, M., Moalla, I., Alimi, A.M.: ARASTI: a database for Arabic scene text recognition. In: 2017 1st International Workshop on Arabic Script Analysis and Recognition (ASAR), pp. 140–144. IEEE (2017)
Ashraf, A.H., et al.: Weapons detection for security and video surveillance using CNN and YOLO-v5s. CMC-Comput. Mater. Contin 70, 2761–2775 (2022)
Chen, R.C.: Automatic License Plate Recognition via sliding-window darknet-YOLO deep learning. Image Vis. Comput. 87, 47–56 (2019)
Dewi, C., Chen, R.C., Jiang, X., Yu, H.: Deep convolutional neural network for enhancing traffic sign recognition developed on YOLO v4. Multimedia Tools Appl. 81(26), 37821–37845 (2022)
Zhang, L., Xu, F., Liu, Y., Zhang, D., Gui, L., Zuo, D.: A posture detection method for augmented reality–aided assembly based on YOLO-6D. Int. J. Adv. Manufact. Technol. 125(7–8), 3385–3399 (2023)
Zhang, D., Mao, R., Guo, R., Jiang, Y., Zhu, J.: YOLO-table: disclosure document table detection with involution. Int. J. Doc. Anal. Recogn. (IJDAR) 26(1), 1–14 (2023)
Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data 6(1), 1–48 (2019)
Schaefer, S., McPhail, T., Warren, J.: Image deformation using moving least squares. In: ACM SIGGRAPH 2006 Papers, pp. 533–540 (2006)
Zeiler, M.D., Taylor, G.W., Fergus, R.: Adaptive deconvolutional networks for mid and high level feature learning. In: Proceedings of the 2011 International Conference on Computer Vision, Barcelona, Spain, 6–13 November 2011, pp. 2018–2025 (2011)
Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., Hu, Q.: ECA-Net: efficient channel attention for deep convolutional neural networks. In: Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 14 June 2020 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Turki, H., Elleuch, M., Kherallah, M. (2024). Using an Optimal then Enhanced YOLO Model for Multi-Lingual Scene Text Detection Containing the Arabic Scripts. In: Yan, W.Q., Nguyen, M., Nand, P., Li, X. (eds) Image and Video Technology. PSIVT 2023. Lecture Notes in Computer Science, vol 14403. Springer, Singapore. https://doi.org/10.1007/978-981-97-0376-0_34
Download citation
DOI: https://doi.org/10.1007/978-981-97-0376-0_34
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-0375-3
Online ISBN: 978-981-97-0376-0
eBook Packages: Computer ScienceComputer Science (R0)