Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{196416,
        author = {SIRIKI SATYANARAYANA and M.B.V.GANGADARARAO and S.AHMED and R.JYOTHI BABU and P.ADITYA SHIVA SHANKAR},
        title = {Language Recognization From Handwritig Based On Machine Learning And Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {2822-2827},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196416},
        abstract = {Handwritten text recognition plays a vital role in document digitization, multilingual communication, and intelligent human–computer interaction. With the growing diversity of handwritten scripts and languages, automatic language recognition from handwriting has become a challenging research problem. This project presents a language recognition system from handwritten text using machine learning and deep learning techniques. The proposed approach involves preprocessing handwritten input images through noise removal, normalization, and segmentation, followed by feature extraction to capture structural and statistical characteristics of handwriting. Traditional machine learning classifiers such as Support Vector Machines (SVM) and Random Forests are explored, along with deep learning models like Convolutional Neural Networks (CNNs) for automatic feature learning. The system is trained and evaluated on handwritten samples from multiple languages to accurately identify the language without prior knowledge of the script. Experimental results demonstrate that deep learning–based models outperform conventional machine learning methods in terms of accuracy and robustness. This work highlights the effectiveness of combining image processing and deep learning techniques for reliable handwritten language recognition and its potential applications in document analysis, translation systems, and smart archival solutions. Keywords: Handwritten Language Recognition, Machine Learning, Deep Learning, Convolutional Neural Networks, Image Processing, Multilingual Handwriting.},
        keywords = {},
        month = {April},
        }