Sakshi. S. Sawant, Aparna. S. Shirkande, N. Shinde, Sharanya Rao
{"title":"OCR Of Devanagari Script Using CNN","authors":"Sakshi. S. Sawant, Aparna. S. Shirkande, N. Shinde, Sharanya Rao","doi":"10.46610/jooce.2023.v09i02.001","DOIUrl":null,"url":null,"abstract":"Devanagari script is widely used across India. It forms many languages like Hindi, Marathi, Nepali and Sanskrit languages. As the Devanagari characters are similar to the hindi character the national language of India. It is important to recognize the characters to understand the message that particular tries to tell. The automatic character recognition system is thus developing for the Devanagari script. The character recognition process converts an image of a character into machine-readable format also its English corresponds. In this paper, we are using Convolutional Neural Network for developing the character recognition system. Convolutional neural network learns directly from data. It is a type of Deep learning neural network architecture. CNN is useful as it does not require any human intervention and performs the identification of important features on its own. The proposed paper uses a CNN algorithm applied to a dataset of 49 characters of Devanagari script. The dataset contains of total 4018 Images. The algorithm of the Convolutional Neural Network is applied to train the dataset. The input image to be predicted is first preprocessed and then the model predicts the output result. The system is designed in Jupyter Lab using Python. The Convolutional Neural Network model's overall accuracy is 98%.","PeriodicalId":159105,"journal":{"name":"Journal of Optical Communication Electronics","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Optical Communication Electronics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.46610/jooce.2023.v09i02.001","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Devanagari script is widely used across India. It forms many languages like Hindi, Marathi, Nepali and Sanskrit languages. As the Devanagari characters are similar to the hindi character the national language of India. It is important to recognize the characters to understand the message that particular tries to tell. The automatic character recognition system is thus developing for the Devanagari script. The character recognition process converts an image of a character into machine-readable format also its English corresponds. In this paper, we are using Convolutional Neural Network for developing the character recognition system. Convolutional neural network learns directly from data. It is a type of Deep learning neural network architecture. CNN is useful as it does not require any human intervention and performs the identification of important features on its own. The proposed paper uses a CNN algorithm applied to a dataset of 49 characters of Devanagari script. The dataset contains of total 4018 Images. The algorithm of the Convolutional Neural Network is applied to train the dataset. The input image to be predicted is first preprocessed and then the model predicts the output result. The system is designed in Jupyter Lab using Python. The Convolutional Neural Network model's overall accuracy is 98%.