dhijam@gitam.edu
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Deena Hijam holds an M.Tech degree from Jawaharlal Nehru University (JNU), New Delhi, and a Ph.D. in Computer Science and Engineering from Tezpur University, Assam, India. She is a recipient of JRF and SRF under the UGC-NET, and she is also GATE qualified. She has two years of professional experience in the corporate sector. Her current research interests focus on the application of deep learning and natural language processing (NLP) to low-resource scripts and languages.
Research Publications
- Hijam, D., Saharia, S. On developing complete character set Meitei Mayek handwritten character database. Vis Comput 38, 525–539 (2022). https://doi.org/10.1007/s00371-020-02032-y
- Hijam, D., Saharia, S. A multilevel recognition of Meitei Mayek handwritten characters using fusion of features strategy. Vis Comput 40, 211–225 (2024). https://doi.org/10.1007/s00371-023-02776-3
- Hijam, D., Saharia, S. Zone and rule assisted recognition of Meitei-Mayek handwritten characters. Evol. Intel. 17, 2963–2980 (2024). https://doi.org/10.1007/s12065-024-00920-z
- Hijam, D., Saharia, S. (2018). Convolutional Neural Network Based Meitei Mayek Handwritten Character Recognition. In: Tiwary, U. (eds) Intelligent Human Computer Interaction. IHCI 2018. Lecture Notes in Computer Science(), vol 11278. Springer, Cham. https://doi.org/10.1007/978-3-030-04021-5_19
- D. Hijam, S. Saharia and Y. Nirmal,
Expertise
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Handwritten Character Recognition
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Machine Learning
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Deep Learning
