Multilingual Semantic Object Identification Using Computer Vision and NLP

Suchetha N V; Abhijeet P M; Likesh K; Vinuth A N; Suhas H

doi:10.55524/ijircst.2026.14.1.1

Abstract

In today’s digital era, accessibility to technology across languages is essential to bridge communication gaps and foster inclusivity. Translation bridges communications between cultures, just as a Multilingual Semantic Object Identification Using Computer Vision and NLP does for visual recognition; instead of keeping users to one language, this system identifies real-world objects and shares results in multiple languages, making technology feel more accessible and user-friendly for diverse communities. The project will further demonstrate how perfectly vision and language can go hand in hand by combining deep learning-based object detection with natural language processing. This approach uses models pre-trained on recognizing objects, combined with translation tools and speech output, ensuring accuracy and real-time performance. Ultimately, the outcome of this system is to provide a helpful tool for its users that offers beneficial support for learning, accessibility, and digital inclusion. Consequently, this grounds the basis for more inclusive AI technologies in a multilingual world.

Keywords

NLP, Computer vision, YOLO, Blip, Object Identification.

References

William Perrizo, W. Jockheck, A. Perrera, D. Ren, and Y. Zhang, “Object boundary detection for ontology-based image classification,” ACM SIGKDD Explorations Newsletter, vol. 4, no. 2, pp. 45–52, 2002. Available from: https://tinyurl.com/mr2fs35z
Andreas Opelt, Axel Pinz, and Andrew Zisserman, “A boundary-fragment model for object detection,” in Computer Vision – ECCV 2006, Lecture Notes in Computer Science, vol. 3954, Springer, Berlin, Heidelberg, 2006, pp. 575–588. Available from: https://link.springer.com/chapter/10.1007/11744047_44
Muhammad Yasir, Md. Sakaouth Hossain, and Sulaiman Khan, “Object identification using manipulated edge detection techniques,” Science, vol. 3, no. 1, pp. 1–7, 2022. Available from: https://tinyurl.com/zrc6ksmr
R. Hussin, M. R. Juhar, W. Kang, and A. Kamarudin, “Digital image processing techniques for object detection from complex background image,” Procedia Engineering, vol. 41, no. 4, pp. 340–346, 2012. Available from: https://doi.org/10.1016/j.proeng.2012.07.182
P. Papageorgiou, M. Oren, and T. Poggio, “A general framework for object detection,” in Proceedings of the Sixth International Conference on Computer Vision (ICCV),
Bombay, India, 1998, pp. 555–562. Available from: https://tinyurl.com/3smzz4p5
P. Viola and M. Jones, “Object detection using image processing,” International Journal of Electrical and Computer Engineering (IJECE), vol. 12, no. 3, pp. 2875–2883, 2022. Available from: https://tinyurl.com/4mnmdh24
S. Sankar Ganesh, K. Mohana Prasad, and Y. Karuna, “Object identification using wavelet transform,” Indian Journal of Science and Technology, vol. 9, no. 5, pp. 1–6, 2016. Available from: https://tinyurl.com/42pamxha
Qiankun Liu, Yichen Li, and Yuqi Jiang, “Generic multi-object tracking,” IEEE Transactions on Image Processing, vol. 33, pp. 2456–2469, 2024. Available from: https://ieeexplore.ieee.org/abstract/document/10571840
Samuele Buro and Isabella Mastroeni, “The multi-language construction,” in Formal Methods for Industrial Critical Systems, Lecture Notes in Computer Science, vol. 12709, Springer, Cham, 2021, pp. 247–262. Available from: https://link.springer.com/chapter/10.1007/978-3-030-65474-0_14
Zhong-Qiu Zhao, Shou-Tao Xu, and Xindong Wu, “Object detection with deep learning,” IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 11, pp. 3212–3232, 2019. Available from: https://ieeexplore.ieee.org/abstract/document/8627998

Cites this article as

S. N. V, A. P. M, L. K, V. A. N, S. H, "Multilingual Semantic Object Identification Using Computer Vision and NLP", International Journal of Innovative Research in Computer Science and Technology (IJIRCST), Vol-14, Issue-1, Page No-1-8, 2026. Available from: https://doi.org/10.55524/ijircst.2026.14.1.1

Corresponding Author

Suchetha N V

Associate Professor, Department of Computer Science & Engineering, Sri Dharmasthala Manjunatheshwara Institute of Technology, Ujire, Karnataka, India

Download Full Paper

Download PDF

No. of Downloads: 33 | No. of Views: 322

Handwritten Notes Digitization and Multilingual Summarization Using OCR and Generative AI

Suchetha N V, Anisha Upadhayaya H S, Anushree U Rao, H N Swati, Mallana Gowda G S.

January 2026 - Vol 14, Issue 1
AgriPulse: Smart Grain Storage System

Dhawal Jain, Omkar S, Pragathi, Sanidhya M Jain, Vishal Raju Angadi.

January 2026 - Vol 14, Issue 1
Generation of Human Faces Using Generative Adversarial Network

Supritha P O, Prajwal, Saikumar Laxman Pujari, Siddartha R, Adarsh Shendage.

January 2026 - Vol 14, Issue 1

Multilingual Semantic Object Identification Using Computer Vision and NLP

Citations

Download Full Paper PDF

Total View 322

Total Download 33