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.

The Sanskrit language : an introductory grammar and reader

Abstract: Now available in paperback, this grammar offers a completely new approach to the study of Sanskrit, aimed at students with no previous specialist knowledge of the categories of grammar. It is a stimulating and infectious approach, designed to cultivate rapid and lasting enthusiasm for Sanskrit. Important features of the work are the use of connected passages for exercise which are intrinsically more interesting and challenging than the unrelated sentences found in other grammars; the great deal of attention given to the explanation of the Devanagari system; and the extensive appendices and glossaries.

On performance analysis of end-to-end OCR systems of Indic scripts

Abstract: Performance evaluation of End-to-End OCR systems of Indic scripts requires matching of UNICODE sequences of OCR output and ground truth. In the literature, Levenshtein edit distance has been used to compute error rates of OCR systems but the accuracies are not explicitly reported. In the present work, we have proposed an accuracy measure based on edit distance and used it in conjunction with error rate to report the performance of an OCR system. We have analyzed the relationship between accuracy and error rates in a quantitative manner. Our analysis has shown that accuracy and error rate are independent of each other and so both are needed to report complete performance of an OCR system. Proposed approach is applicable to all the Indic scripts and the experimental results on different scripts like Devanagari, Telugu, Kannada etc. are shown.

LSTM Based Lip Reading Approach for Devanagiri Script

Abstract: Speech Communication in a noisy environment is a difficult and challenging task. Many professionals work in noisy environments like aviation, constructions, or manufacturing, and find it difficult to communicate orally. Such noisy environments need an automated lip-reading system that could be helpful in communicating some instructions and commands. This paper proposes a novel lip-reading solution, which extracts the geometrical shape of lip movement from the video and predicts the words/sentences spoken. An Indian specific language data set is developed which consists of lip movement information captured from 50 persons. This includes students in the age group of 18 to 20 years and faculty in the age group of 25 to 40 years . All have spoken a paragraph of 58 words within 10 sentences in Hindi (Devanagari, spoken in India) language which was recorded under various conditions. The implementation consists of facial parts detection, along with Long short term memory’s. The proposed solution is able to predict the words spoken with 77% and 35% accuracy for data set of 3 and 10 words respectively. The sentences are predicted with 20% accuracy, which is encouraging.

Handwritten Hindi Character Recognition

Abstract: Devanagari, the most accepted script in India and Hindi is the only dialect which is widely spoken and written, so Handwritten Hindi character Recognition is done. Optical Character Recognition (OCR) is used for pattern recognition, it can be online or offline. Handwritten text is electronically converted into machine learning language. Handwritten character Recognition has many applications like cheque reader,passport reader,address reader,specific tasks readers. Devanagari is troublesome because the characters present in a words are somewhat similar to other character or connected words may have problem in recognition as number of modifiers are present. The major challenge faced was removal of header line as header line cannot be always straight as it varies from person to person. The characters which are handwritten will not always have sharp corners, the header lines present will not be perfectly straight and the curves which are present will not be so smooth. Handwritten character recognition undergo three major steps (i) Pre-Processing (ii) Feature Extraction(iii)Classification. Pre processing is the first step which deals with binarization, noise removal, morphological operations and segmentation. Segmentation is major part in character recognition. Words are segmented into single single characters and these segmented characters are used for feature extraction. In second step Histogram of Oriented Gradients (HOG) is used as extraction of feature in an image so as to obtain the feature vector .Object detection can be easily done by using HOG in image processing and computer vision. HOG has intensity values which is obtained by gradient computation and will give rough idea of shape or pattern of an image. Last step concludes with classification, for classifying the samples Support Vector machine is implemented. SVM is basically used as binary classifier but in this project it has been used as Multiclass Classifier (One Vs. All). SVM constructs a hyper plane as data points are mapped into higher D-dimensional space. Non Linear SVM includes various kernels like polynomial kernel, radial basis kernel for mapping the data into higher D-dimensional space. The performance analysis is efficient for the kernels which are used. Accuracy rate can be improved for segmentation by using various other methods for segmentation. It can be extended to work on degraded text or broken characters and conversion of text to speech. Online recognition of character can be done.

Clustering of Multi Scripts Isolated Characters Using k-Means Algorithm

Abstract: The aim of this paper is script identification problem of handwritten text which facilitates the clustering of data according to their type of script In this paper, collection of different types of handwritten text document ie Devanagari, Gurumukhi and Roman is taken as input and then cluster of all these documents according to script type whether ie Devanagari, Gurumukhi, or Roman was prepared Clustering of handwritten multi-script document scheme proposed in this paper is divided into two phases First phase used to extract the features of given text images In the second phase, features extracted in the previous phase were used for clustering with kMeans algorithm In feature extraction phase, we have extracted four types of features, namely, circular curvature feature, horizontal stroke density feature, pixel density feature value and zoning based feature In this study, we have considered 4,850 samples of isolated characters of Devanagari, Gurumukhi and Roman script