Relevant bibliographies by topics / Devanagari script

Contents

  1. Journal articles

Academic literature on the topic 'Devanagari script'

Author: Grafiati
Published: 4 June 2021
Last updated: 12 June 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Devanagari script.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Devanagari script"

1

Vijay, Vijay, M. U Kharat, and S. V Gumaste. "Study of Different Features and Classification Techniques for Recognition of Handwritten Devanagari Text." International Journal of Engineering & Technology 7, no. 4.19 (2018): 1055. http://dx.doi.org/10.14419/ijet.v7i4.19.28285.

Full text
Abstract:
Devanagari script is most popular and an older script in India. Millions of people all over the globe are using Devanagri script for various purposes such as communication, understanding the history, record keeping, research, etc. Recognition of handwritten Devanagari word is one of the popular area of research from decades because of its wide scope of applications. Different features and techniques of classification are the most important steps in the process of recognizing Devanagari handwritten word, are described in this paper.
APA, Harvard, Vancouver, ISO, and other styles
2

PANDEY, Krishna Kumar, and Smita JHA. "Tracing the Identity and Ascertaining the Nature of Brahmi-derived Devanagari Script." Acta Linguistica Asiatica 9, no. 1 (2019): 59–73. http://dx.doi.org/10.4312/ala.9.1.59-73.

Full text
Abstract:
Current research exploits the orthographic design of Brahmi-derived scripts (also called Indic scripts), particularly the Devanagari script. Earlier works on orthographic nature of Brahmi-derived scripts fail to create a consensus among epigraphists, historians or linguists, and thus have been identified by various names, like semi-syllabic, subsyllabic, semi-alphabetic, alphasyllabary or abugida. On the contrary, this paper argues that Brahmi-derived scripts should not be categorized as scripts with overlapping features of alphabetic and syllabic properties as these scripts are neither alphabetic nor syllabic. Historical evolution and linguistic properties of Indic scripts, particularly Devanagari, ascertain the need for a new categorization of its own and, thus preferably merit a unique descriptor. This paper investigates orthographic characteristics of the Brahmi-derived Devanagari script, current trends in research pertaining to the Devanagari script along with other Indic scripts and the implications of these findings for literacy development in Indic writing systems.
APA, Harvard, Vancouver, ISO, and other styles
3

Ahmad, Rizwan. "Urdu in Devanagari: Shifting orthographic practices and Muslim identity in Delhi." Language in Society 40, no. 3 (2011): 259–84. http://dx.doi.org/10.1017/s0047404511000182.

Full text
Abstract:
AbstractIn sociolinguistics, Urdu and Hindi are considered to be textbook examples of digraphia—a linguistic situation in which varieties of the same language are written in different scripts. Urdu has traditionally been written in the Arabic script, whereas Hindi is written in Devanagari. Analyzing the recent orthographic practice of writing Urdu in Devanagari, this article challenges the traditional ideology that the choice of script is crucial in differentiating Urdu and Hindi. Based on written data, interviews, and ethnographic observations, I show that Muslims no longer view the Arabic script as a necessary element of Urdu, nor do they see Devanagari as completely antithetical to their identity. I demonstrate that using the strategies of phonetic and orthographic transliteration, Muslims are making Urdu-in-Devanagari different from Hindi, although the difference is much more subtle. My data further shows that the very structure of a writing system is in part socially constituted. (Script-change, Urdu, Urdu-in-Devanagari, Hindi, Arabic script, Devanagari, orthography, transliteration)*
APA, Harvard, Vancouver, ISO, and other styles
4

Agnihotri, Ved Prakash. "Offline Handwritten Devanagari Script Recognition." International Journal of Information Technology and Computer Science 4, no. 8 (2012): 37–42. http://dx.doi.org/10.5815/ijitcs.2012.08.04.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

MALIK, LATESH, and P. S. DESHPANDE. "RECOGNITION OF HANDWRITTEN DEVANAGARI SCRIPT." International Journal of Pattern Recognition and Artificial Intelligence 24, no. 05 (2010): 809–22. http://dx.doi.org/10.1142/s0218001410008123.

Full text
Abstract:
Segmentation of handwritten text into lines, words and characters is one of the important steps in the handwritten text recognition process. In this paper, we propose a float fill algorithm for segmentation of unconstrained Devanagari text into words. Here, a text image is directly segmented into individual words. Rectangular boundaries are drawn around the words and horizontal lines are detected with template matching. A mask is designed for detecting the horizontal line and is applied to each word from left to right and top to bottom of the document. Header lines are removed for character separation. A new segment code features are extracted for each character. In this paper, we present the results of multiple classifier combination for offline handwritten Devanagari characters. The use of regular expressions in handwritten characters is a novel concept and they are defined in a manner so that they can become more robust to noise. We have achieved an accuracy of 94% for word level segmentation, 95% for coarse classification and 85% for fine classification of character recognition. On experimentation with a dataset of 5000 samples of characters, the overall recognition rate observed is 95% as we considered top five choice results. The proposed combined classifier can be applied to handwritten character recognition of any other language like English, Chinese, Arabic, etc. and can recognize the characters with same accuracy.18 For printed characters we have achieved accuracy of 100%, only by applying the regular expression classifier.17
APA, Harvard, Vancouver, ISO, and other styles
6

Susan, Seba, and Jatin Malhotra. "Recognising Devanagari Script by Deep Structure Learning of Image Quadrants." DESIDOC Journal of Library & Information Technology 40, no. 05 (2020): 268–71. http://dx.doi.org/10.14429/djlit.40.05.16336.

Full text
Abstract:

 
 
 Ancient Indic languages were written in the Devanagari script from which most of the modern-day Indic writing systems have evolved. The digitisation of ancient Devanagari manuscripts, now archived in national museums, is a part of the language documentation and digital archiving initiative of the Government of India. The challenge in digitizing these handwritten scripts is the lack of adequate datasets for training machine learning models. In our work, we focus on the Devanagari script that has 46 categories of characters that makes training a difficult task, especially when the number of samples are few. We propose deep structure learning of image quadrants, based on learning the hidden state activations derived from convolutional neural networks that are trained separately on five image quadrants. The second phase of our learning module comprises of a deep neural network that learns the hidden state activations of the five convolutional neural networks, fused by concatenation. The experiments prove that the proposed deep structure learning outperforms the state of the art.
 
 
APA, Harvard, Vancouver, ISO, and other styles
7

Obaidullah, S. K., K. C. Santosh, Chayan Halder, Nibaran Das, and Kaushik Roy. "Word-Level Multi-Script Indic Document Image Dataset and Baseline Results on Script Identification." International Journal of Computer Vision and Image Processing 7, no. 2 (2017): 81–94. http://dx.doi.org/10.4018/ijcvip.2017040106.

Full text
Abstract:
Document analysis research starves from the availability of public datasets. Without publicly available dataset, one cannot make fair comparison with the state-of-the-art methods. To bridge this gap, in this paper, the authors propose a word-level document image dataset of 13 different Indic languages from 11 official scripts. It is composed of 39K words that are equally distributed i.e., 3K words per language. For a baseline results, five different classifiers: multilayer perceptron (MLP), fuzzy unordered rule induction algorithm (FURIA), simple logistic (SL), library for linear classifier (LibLINEAR) and bayesian network (BayesNet) classifiers are used with three state-of-the-art features: spatial energy (SE), wavelet energy (WE) and the Radon transform (RT), including their possible combinations. The authors observed that MLP provides better results when all features are used, and achieved the bi-script accuracy of 99.24% (keeping Roman common), 98.38% (keeping Devanagari common) and tri-script accuracy of 98.19% (keeping both Devanagari and Roman common).
APA, Harvard, Vancouver, ISO, and other styles
8

Obaidullah, Sk Md, Chitrita Goswami, K. C. Santosh, Nibaran Das, Chayan Halder, and Kaushik Roy. "Separating Indic Scripts with matra for Effective Handwritten Script Identification in Multi-Script Documents." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 05 (2017): 1753003. http://dx.doi.org/10.1142/s0218001417530032.

Full text
Abstract:
We present a novel approach for separating Indic scripts with ‘matra’, which is used as a precursor to advance and/or ease subsequent handwritten script identification in multi-script documents. In our study, among state-of-the-art features and classifiers, an optimized fractal geometry analysis and random forest are found to be the best performer to distinguish scripts with ‘matra’ from their counterparts. For validation, a total of 1204 document images are used, where two different scripts with ‘matra’: Bangla and Devanagari are considered as positive samples and the other two different scripts: Roman and Urdu are considered as negative samples. With this precursor, an overall script identification performance can be advanced by more than 5.13% in accuracy and 1.17 times faster in processing time as compared to conventional system.
APA, Harvard, Vancouver, ISO, and other styles
9

Malanker, Aradhana A., and Prof Mitul M Patel. "Handwritten Devanagari Script Recognition: A Survey." IOSR Journal of Electrical and Electronics Engineering 9, no. 2 (2014): 80–87. http://dx.doi.org/10.9790/1676-09228087.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Kumar, Vijay, and Pankaj K. Sengar. "Segmentation of Printed Text in Devanagari Script and Gurmukhi Script." International Journal of Computer Applications 3, no. 8 (2010): 24–29. http://dx.doi.org/10.5120/749-1058.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources
You might also be interested in the extended bibliographies on the topic 'Devanagari script' for particular source types:
Journal articles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography