How to cite (IEEE): J. Bati, & P. Raj Dawadi "Ranjana Script Handwritten Character Recognition using CNN," JOIV : International Journal on Informatics Visualization, vol. 7, no. 3, , pp. 984-990, Sep. 2023. https://doi.org/10.30630/joiv.7.3.1725

How to cite (APA): Bati, J., & Raj Dawadi, P. (2023). Ranjana Script Handwritten Character Recognition using CNN. JOIV : International Journal on Informatics Visualization, 7(3), 984-990. https://doi.org/10.30630/joiv.7.3.1725

How to cite (Chicago): Bati, Jen, AND Raj Dawadi, Pankaj. "Ranjana Script Handwritten Character Recognition using CNN" JOIV : International Journal on Informatics Visualization [Online], Volume 7 Number 3 (10 September 2023) https://doi.org/10.30630/joiv.7.3.1725

How to cite (Vancouver): Bati J, & Raj Dawadi P . Ranjana Script Handwritten Character Recognition using CNN. JOIV : International Journal on Informatics Visualization [Online]. 2023 Sep; 7(3):984-990. https://doi.org/10.30630/joiv.7.3.1725

How to cite (Harvard): Bati, J., & Raj Dawadi, P. ,2023. Ranjana Script Handwritten Character Recognition using CNN. JOIV : International Journal on Informatics Visualization, [Online] 7(3), pp. 984-990. https://doi.org/10.30630/joiv.7.3.1725

How to cite (MLA8): Bati, Jen, & Pankaj Raj Dawadi. "Ranjana Script Handwritten Character Recognition using CNN." JOIV : International Journal on Informatics Visualization [Online], 7.3 (2023): 984-990. Web. 5 Dec. 2023 , https://doi.org/10.30630/joiv.7.3.1725

BibTex Citation Data :

@article{JOIV1725,
    	author = {Jen Bati and Pankaj Raj Dawadi},
    	title = {Ranjana Script Handwritten Character Recognition using CNN},
    	journal = {JOIV : International Journal on Informatics Visualization},
    	volume = {7},
    	number = {3},
    	year = {2023},
    	keywords = {Ranjana script; Newari script; RHCD; hyper-parameter; handwritten characters},
    	abstract = {This paper proposes a public image database for Ranjana script Handwritten Character Datasets (RHCD), publicly available for Ranjana script researchers or anyone interested in the subject. To the best of our knowledge, the Ranjana script Handwritten Character Dataset (RHCD) is the first publicly available database for Ranjana script researchers. Ranjana script descended from the Brahmi script, consists of 36 consonant letters, 16 vowel letters, and 10 numerical letters. The focus of this research is three-fold: the first is to create a new database for Ranjana script Handwritten Character Recognition; the second is to test the character recognition accuracy of the created RHCD using existing CNN algorithms like LeNET-5, AlexNET, and ZFNET algorithm; the third is to propose a model by investigating different hyper-tuning parameters to improve the recognition accuracy of the created RHCD. The research method applied in this study is dataset collection, digitization & cropping, pre-processing, dataset splitting, data augmentation, and finally, implementing the CNN model (existing and proposed). Performance evaluation is based on the test accuracy, precision, recall, and F1-score. The experiment result shows that our model ranks first, with a testing accuracy of 99.73% for 64x64 pixels resolution with precision, recall, and F1-score value 1. Creation and recognition of Ranjana script characters, vowel modifiers, and compound characters can be the next milestone to be achieved. Segmentation of words and sentences into characters and recognizing each character individually can be the next research domain.},
        	issn = {2549-9904},    	pages = {984--990},    	doi = {10.30630/joiv.7.3.1725},
        	url = {http://joiv.org/index.php/joiv/article/view/1725}
    }
    

Refworks Citation Data :

@article{{JOIV}{1725}, author = {Bati, J., Raj Dawadi, P.}, title = {Ranjana Script Handwritten Character Recognition using CNN}, journal = {JOIV : International Journal on Informatics Visualization}, volume = {7}, number = {3}, year = {2023}, doi = {10.30630/joiv.7.3.1725}, url = {} }