Authors : G.R.L.M.Tayaru; Yasaswi Venkata Naga Sravani Ravuri; Saranya Padala; Satya Vani Reguri; Surya Venkata Anjana Sathvika Kanamarlapudi; Charmy Rose Kommini

Volume/Issue : Volume 10 - 2025, Issue 1 - January

Google Scholar : https://tinyurl.com/4wdxu3aj

Scribd : https://tinyurl.com/ytbe7xz8

DOI : https://doi.org/10.5281/zenodo.14890841

Abstract : An advance system named a ‘Deep Learning Note-Taking App with CNN and NLP for Handwritten and Voice Notes’ has been made to change the face of note taking by soft connecting computer vision and natural language processing technologies. Using Convolutional Neural Networks (CNN) for character and word recognition, and using state of the art NLP models for voice note transcriptions, this application processes handwritten notes. The content is extracted, structured, searchable and easily shareable which greatly increases productivity and accessibility. Multilingual transcription, contextual keyword tagging, and real time synchronization among devices are supported by the app. This project is an attempt to bring simplicity to note taking, facilitate data retrieval, and enable good information management by combining deep learning algorithms with an easy to use interface.

Keywords : Deep Learning, Convolutional Neural Networks (CNN), Natural Language Processing (NLP), Handwritten Notes Recognition, Voice Notes Transcription, Multilingual Support, Note-Taking Application.

References :

1. Shinde, N. K., More, C. S., Singh, S., Aryan, A., & Ranjan, A. (2024, April). Optimizing Handwritten Text Recognition for Automated Note Generation for Enhanced Learning Environment. In 2024 MIT Art, Design and Technology School of Computing International Conference (MITADTSoCiCon) (pp. 1-6). IEEE.
2. Zhou, Y., Tang, C., & Shimada, A. (2024, May). A Novel Approach: Enhancing Data Extraction from Student Handwritten Notes Using Multi-Task U-net and GPT-4. In 2024 7th International Symposium on Autonomous Systems (ISAS) (pp. 1-6). IEEE.
3. Newalkar, A., Khade, H., Khandare, D., & Patel, D. CNN-Powered Handwriting to Digital Text Converter.
4. Achaliya, P. (2024). Machine Learning Based Handwritten Character Recognition. Library Progress International, 44(3), 11128-111135.
5. Nikhitha, N., & Reddy, N. V. S. (2023, July). Advancing Optical Character Recognition for Handwritten Text: Enhancing Efficiency and Streamlining Document Management. In 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-8). IEEE.
6. Azhar, Z., Chaudhry, H. N., Kulsoom, F., & Narejo, S. (2024). Deep Learning-Based Automated Classroom Slide Extraction.
7. Maheswari, B., Subha, S., & VK, T. M. (2023, June). Offline Recognition Of Handwritten Text Using Combination Of Neural Networks. In 2023 8th International Conference on Communication and Electronics Systems (ICCES) (pp. 865-870). IEEE.
8. e Silva, L. C., Sobrinho, Á. A. D. C. C., Cordeiro, T. D., Melo, R. F., Bittencourt, I. I., Marques, L. B., ... & Isotani, S. (2023). Applications of convolutional neural networks in education: A systematic literature review. Expert Systems with Applications, 231, 120621.
9. Gupta, R., Mehrotra, D., Bouhamoum, R., Masmoudi, M., & Baazaoui, H. (2023, June). Handwriting analysis ai-based system for assisting people with dysgraphia. In International Conference on Computational Science (pp. 185-199). Cham: Springer Nature Switzerland.
10. Grygoriev, A., Degtyarenko, I., Deriuga, I., Polotskyi, S., Melnyk, V., Zakharchuk, D., & Radyvonenko, O. (2021, September). HCRNN: a novel architecture for fast online handwritten stroke classification. In International Conference on Document Analysis and Recognition (pp. 193-208). Cham: Springer International Publishing.