International Journal of Innovative Research in Computer Science and Technology
Year: 2026, Volume: 14, Issue: 1
First page : ( 9) Last page : ( 16)
Online ISSN : 2347-5552
Suchetha N V
DOI: 10.55524/ijircst.2026.14.1.2 |
DOI URL: https://doi.org/10.55524/ijircst.2026.14.1.2
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http://creativecommons.org/licenses/by/4.0
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Suchetha N V , Anisha Upadhayaya H S, Anushree U Rao, H N Swati, Mallana Gowda G S
This research proposes a fully client-side system for digitising and summarising handwritten notes that integrate optical character recognition (OCR) with advanced natural language generation models. Initial experimentation with a Convolutional Neural Network (CNN)– based handwritten character recognition system revealed significant challenges, including low recognition accuracy, strong dependence on dataset quality, and poor generalisation across diverse handwriting styles. A subsequent implementation using Gemini 2.5 Pro provided high-quality English summaries but failed to deliver equivalent performance for multilingual academic content. To overcome these issues, this study adopts a hybrid methodology combining Tesseract.js OCR for multilingual text extraction with Gemini 2.5 Flash for fast, context-aware, and language-flexible summarization. The system is able to take handwritten notes, convert them into clear and readable text, and produce well-structured academic summaries across different subjects and languages. Our tests show that it works reliably, adapts well to multiple languages, and is easy to use, making it a strong and practical tool for educational digitization.
Associate Professor, Department of Computer Science & Engineering, Sri Dharmasthala Manjunatheshwara Institute of Technology, Ujire, Karnataka, India
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