Downloads provided by UsageCounts
{"references": ["Purohit, A., & Chauhan, S. S. (2016). A literature survey on handwritten character recognition. IJCSIT) International Journal of Computer Science and Information Technologies, 7(1), 1-5.", "Acharya, S., Pant, A. K., & Gyawali, P. K. (2015, December). Deep learning based large scale handwritten Devanagari character recognition. In 2015 9th International conference on software, knowledge, information management and applications (SKIMA) (pp. 1-6). IEEE.", "Kumar, G., Bhatia, P. K., & Banger, I. (2013). Analytical review of preprocessing techniques for offline handwritten character recognition. International Journal of Advances in Engineering Sciences, 3(3), 14- 22..", "Rahiman, M. A., Rajasree, M. S., Masha, N., Rema, M., Meenakshi, R., & Kumar, G. M. (2011, April). Recognition of handwritten Malayalam characters using vertical & horizontal line positional analyzer algorithm. In 2011 3rd International Conference on Electronics Computer Technology (Vol. 2, pp. 268-274). IEEE..", "Saha, S., & Som, T. (2011). Handwritten character recognition using fuzzy membership function. IJETSE International Journal of Emerging Technologies in Sciences and Engineering, 5(2).", "Bajaj, R., Dey, L., & Chaudhury, S. (2002). Devnagari numeral recognition by combining decision of multiple connectionist classifiers. Sadhana, 27(1), 59-72..", "Charles, P. K., Harish, V., Swathi, M., & Deepthi, C. H. (2012). A review on the various techniques used for optical character recognition. International Journal of Engineering Research and Applications, 2(1), 659-662.", "Neves, R. F., Lopes Filho, A. N., Mello, C. A., & Zanchettin, C. (2011, October). A SVM based off-line handwritten digit recognizer. In 2011 IEEE international conference on systems, man, and cybernetics (pp. 510-515). IEEE..", "Pradeep, J., Srinivasan, E., & Himavathi, S. (2011, April). Diagonal based feature extraction for handwritten character recognition system using neural network. In 2011 3rd international conference on electronics computer technology (Vol. 4, pp. 364-368). IEEE."]}
Even if the technological and digital world is expanding more quickly, there are still many things that are lacking. What a wonderful thing it would be to be able to trust machines to scan any handwritten characters into digital representation. The method for doing this is called optical character recognition (OCR), but there is still much room for improvement. Although there has been work done on it, the technique developed for one language cannot be applied to another due to language variations. Nepali is not a language that is frequently used online. Perhaps this is why there are fewer OCR systems developed using this language. We have made an effort to improve on it so that Nepali characters can be recognized. Basically, the idea is to use a camera to scan Nepali handwriting from hard copy paper, locate the regions in the image where the characters are present, segment those localized parts into characters, and then digitally display each predicted segmented character.
Optical Character Recognition (OCR), Devanagari handwritten characters, segmentation of handwritten character, training model, Convolutional Neural Network (CNN), Optical Character Recognition (OCR), Devanagari handwritten characters, segmentation of handwritten character, training model, Convolutional Neural Network (CNN)
Optical Character Recognition (OCR), Devanagari handwritten characters, segmentation of handwritten character, training model, Convolutional Neural Network (CNN), Optical Character Recognition (OCR), Devanagari handwritten characters, segmentation of handwritten character, training model, Convolutional Neural Network (CNN)
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
| views | 25 | |
| downloads | 25 |

Views provided by UsageCounts
Downloads provided by UsageCounts