Topic
Devanagari
About: Devanagari is a research topic. Over the lifetime, 655 publications have been published within this topic receiving 7428 citations. The topic is also known as: Deva nagari & Hindi Script.
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23 Jan 2020
TL;DR: In this paper, a neural network is used to recognize the text present in ancient documentation through an input image, which is mainly written in Devanagari script and is used for preserving ancient documentation.
Abstract: In recent times Sanskrit script recognition has a great significance in the field of preserving ancient documentation. Ancient documentation in India is mainly written in Devanagari script. This documentation is mostly written in Sanskrit language. The proposed system uses neural networks to recognize the text present in ancient documentation through an input image. Character Recognition- The process of recognizing handwritten character or Printed character. It is generally known as OCR (optical character recognition) which translates electronic images, handwritten, typewritten, or printed text to machine editable.
21 Jul 2013
TL;DR: The effect of transliteration, on the human readability is explored by studying the changes in the eye-gaze patterns, which are recorded with an eye-tracker during experimentation over the areas of interest.
Abstract: We present our efforts on studying the effect of transliteration, on the human readability. We have tried to explore the effect by studying the changes in the eye-gaze patterns, which are recorded with an eye-tracker during experimentation. We have chosen Hindi and English languages, written in Devanagari and Latin scripts respectively. The participants of the experiments are subjected to transliterated words and asked to speak the word. During this, their eye movements are recorded. The eye-tracking data is later analyzed for eye-fixation trends. Quantitative analysis of fixation count and duration as well as visit count is performed over the areas of interest.
01 Jan 2013
TL;DR: A methodology for extracting text from printed image document is presented and Devanagari Script (Hindi language) from extracted text is identified and compared with edge based and connected component with projection profile approach.
Abstract: Texts that appear in the image contain useful and important information. Optical Character Recognition technology is restricted to finding text printed against clean backgrounds, and cannot handle text printed against shaded or textured backgrounds or embedded in images. It is necessary to extract the text form image which is helpful in a society for a blind and visually impaired person when voice synthesizer is attached with the system. In this paper, we present a methodology for extracting text from printed image document and then identified Devanagari Script (Hindi language) from extracted text. Firstly we used Morphological Approach for extracting the text from image documents. The resultant text image is passed to Optical Character Recognition for Identification purpose. Projection profile is used for segmentation followed by Visual Discriminating approach for feature extraction. Finally for classification purpose Heuristic search is used. The result of proposed method for text extraction is compared with edge based and connected component with projection profile approach. After comparison using precision and recall rate it is observed that proposed algorithm work well.
TL;DR: In this paper , a text-independent dialect recognition system is proposed for Marathi language using six different classifiers; K-nearest neighbor (KNN), Naïve Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT), Stochastic Gradient Descent (SGD) classifier and Ridge Classifier (RC).
Abstract: Text-independent dialect recognition system is proposed in this paper for Marathi language. India is rich in language varieties. Each language in turn has its unique dialect variations. Maharashtra has Marathi as official language and for Goa it is a co-official language . In literature there are very few studies available for Indian language recognition and then their respective dialect recognition. So research work available for regional languages such as Marathi is extremely limited. As a part of research work, an attempt is made to generate a case study of a low resourced Marathi language dialect recognition system. The study was carried out using Marathi speech data corpus provided by Linguistic Data Consortium for Indian Language (LDC- IL). This corpus includes four major dialects of Marathi speakers. The efficiency and performance evaluation of the explored spectral (rhythmic) and temporal features are carried out to perform classification tasks. We investigated the performance of six different classifiers; K-nearest neighbor (KNN), Naïve Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT) classifier , Stochastic Gradient Descent (SGD) classifier and Ridge Classifier (RC). Experimental results have demonstrated that the RC classifier worked well with 84.24% of accuracy for fifteen spectral and temporal features. With twelve MFCCs it has been observed that SGD has outperformed among all classifiers with accuracy of 80.63%. For further study, a prominent feature subset as a part of dimensionality reduction has been identified using chi square, mutual information and ANOVA-f test. In this chi-square based feature extraction method has proven to be the best over over mutual information and ANOVA f-test.