Relevant bibliographies by topics / DEVANAGRI

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'DEVANAGRI'

Author: Grafiati
Published: 11 September 2023
Last updated: 16 April 2024

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Journal articles on the topic "DEVANAGRI"

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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 (November 27, 2018): 1055. http://dx.doi.org/10.14419/ijet.v7i4.19.28285.

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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.
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Singh, Pratibha, Ajay Verma, and Narendra S. Chaudhari. "Devanagri Handwritten Numeral Recognition using Feature Selection Approach." International Journal of Intelligent Systems and Applications 6, no. 12 (November 8, 2014): 40–47. http://dx.doi.org/10.5815/ijisa.2014.12.06.

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Ram, Shrawan, Shloak Gupta, and Basant Agarwal. "Devanagri character recognition model using deep convolution neural network." Journal of Statistics and Management Systems 21, no. 4 (June 19, 2018): 593–99. http://dx.doi.org/10.1080/09720510.2018.1471264.

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Anjum, Naveed, Tarun Bali, and Balwinder Raj. "Design and Simulation of Handwritten Gurumukhi and Devanagri Numerals Recognition." International Journal of Computer Applications 73, no. 12 (July 26, 2013): 16–21. http://dx.doi.org/10.5120/12792-9958.

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S., Sushma, and Sharada B. "Keyword Spotting in Scanned Images of Historical Handwritten Devanagri Documents." International Journal of Computer Applications 181, no. 36 (January 17, 2019): 5–9. http://dx.doi.org/10.5120/ijca2019918322.

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Nathani, Bharti, Nisheeth Joshi, and G. N. Purohit. "Design and development of lemmatizer for Sindhi language in devanagri script." Journal of Statistics and Management Systems 22, no. 4 (May 19, 2019): 635–41. http://dx.doi.org/10.1080/09720510.2019.1609187.

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Kaur, Amanpreet, Mohinder Singh, and Om Prakash Jasuja. "Interscript comparison of handwriting features leading to their identification and authorship." Nowa Kodyfikacja Prawa Karnego 45 (December 29, 2017): 15–36. http://dx.doi.org/10.19195/2084-5065.45.3.

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Identification of handwriting found on the disputed document by comparison with the known handwriting samples of the suspect still comprise the problem which is most com­monly referred to a forensic document examiner. One of the important scientifically estab­lished principles which govern such analysis and identification is the ‘Principle of Compari­son’ which explicitly states that, for obtaining correct results, like has to be compared with like; meaning thereby that the expert has to analyze and rely upon similar letters and com­binations between the questioned and the standard handwriting samples and, consequently, the problems where similar handwriting samples in the same script have not been provided for comparison; usually fall outside the scope of forensic document examination. However, in this field, like any other human activity; perfect and ideal conditions are hard to achieve. Handwriting, being acquired skill and neuro-muscular controlled motor activity, its basic elements like the horizontal stroke, vertical stroke, loops, curves and arches etc., are combined together to form letters and alphabets of all the scripts. The question then arises — whether inter-script comparison of handwriting samples can be attempted lead­ing to some limited or qualified conclusions. Thus, if it becomes possible and practicable to examine and compare the basic elements of questioned handwriting in one script, say Devanagri with the similar elements found in specimen/ admitted handwriting samples in another script by the same writer, say Gurmukhi, because sample handwritings in Devanagri could not be procured for whatsoever reasons; the scope of examination can be widened further and expert may be in a position to express some opinion regarding their common authorship or otherwise, which may be found worthwhile to the investigat­ing agency or the court of law, thereby helping in the administration of justice ultimately.To the best of our knowledge, not much research is available, where writings produced in different scripts by the same writer could be compared, thereby leading to a definite opin­ion on the issue of their common authorship or otherwise. In the present study, an attempt has been made to explore this issue by taking writing samples of the same writer in three scripts, having knowledge of all the three commonly used languages, i.e., English, Hindi, and Punjabi, corresponding to the said scripts i.e., Roman, Devanagari and Gurumukhi. Three hundred sixty 360 writing samples were obtained from as many as 40 individuals appropriately skilled in writing, reading and speaking these languages/ scripts. Careful study and evaluation of the basic elements of written strokes whose execu­tion were found to be similar in all the three scripts has been carried out indicating the possibility of ‘Script Independent Comparison’. Limitations of the proposed study have also been discussed in the paper.
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Singh, Pratibha, Ajay Verma, and Narendra S. Chaudhari. "Reliable Devanagri Handwritten Numeral Recognition using Multiple Classifier and Flexible Zoning Approach." International Journal of Image, Graphics and Signal Processing 6, no. 9 (August 8, 2014): 61–68. http://dx.doi.org/10.5815/ijigsp.2014.09.08.

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Srinivasa Rao, Adabala Venkata, D. R. Sandeep, V. B. Sandeep, and S. Dhanam Jaya. "Segmentation of Touching Hand written Telugu Characters by using Drop Fall Algorithm." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 3, no. 2 (October 30, 2012): 343–46. http://dx.doi.org/10.24297/ijct.v3i2c.2897.

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Recognition of Indian language scripts is a challenging problem. Work for the development of complete OCR systems for Indian language scripts is still in infancy. Complete OCR systems have recently been developed for Devanagri and Bangla scripts. Research in the field of recognition of Telugu script faces major problems mainly related to the touching and overlapping of characters. Segmentation of touching Telugu characters is a difficult task for recognizing individual characters. In this paper, the proposed algorithm is for the segmentation of touching Hand written Telugu characters. The proposed method using Drop-fall algorithm is based on the moving of a marble on either side of the touching characters for selection of the point from where the cutting of the fused components should take place. This method improvers the segmentation accuracy higher than the existing one.
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Richmond, Farley. "Kutiyattam: Marriage of an Ancient Art and the New Technology." Journal of Educational Technology Systems 24, no. 2 (December 1995): 165–71. http://dx.doi.org/10.2190/2w01-7ahb-af02-3xwy.

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This presentation traces the development of a multimedia program on Kutiyattam, the Sanskrit theatre of India, perhaps the world's oldest surviving genre of theatre. The program was designed and developed on HyperCard, including QuickTime movies, scanned slides and photographs, and sound. It includes many articles on the subject, as well as a devanagri text and English translation of the Hastalakshanadipika, an ancient Sanskrit manuscript regarded as the source of the gesture language of the actors. The application focuses on stylized gestures which are an essential part of the language of performance, unique patterns of chanting which represent a character's mood and emotions, physical exercises and massage that are used to develop an actor's stamina, discipline, and flexibility, eye exercises and facial expressions which permit performers to convey the deeper meaning of the performance text, and the musical accompaniment which shares an integral place in the performance event. It also identifies some of the notable advantages and disadvantages working with HyperCard.
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Dissertations / Theses on the topic "DEVANAGRI"

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Singh, V. "Devanagari type in the twentieth century." Thesis, University of Reading, 2017. http://centaur.reading.ac.uk/78269/.

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This thesis examines the twentieth-century history of the design of typefaces for on India's most widely used scripts, Devangari, and addresses the historical framework of Devangari type from the beginnings of mechanical composition to the advent of digital technologies. Focusing on some of the significant initiatives that enabled textual communication of this script, and investigating the dynamics behind these developments, this thesis positions the design process in its entirety - not merely the marketing end-products of design - as a key resource for understanding and explaining technological change in specific geographical contexts. Acknowledging the social and economic imperatives involved in the process, this thesis also extends an analysis of the political dynamics as work in the visual representation of the language. A majority of historical narratives in type and typography have tended to approach technological innovations as autonomous developments, transforming and reinventing cultural practices as they are transmitted beyond their point of origin. The argument of the thesis contest this view by locating both the products and the processes of technology within the realm of cultural agency and contextual adaption. It does so specifically by examining transnational professional networks and by tracing type-making projects across geographies separated by widely differing notions and circumstances of development. This research aims to make a significant contribution to knowledge in the field, for the first time building on documentary evidence from primary sources spread over three continents. It draws on original archival material such as letter-drawings, artwork, process documents, company correspondence, business records, trade literature and industry documentation, as well as private collections across seven countries - including India, UK, US and Japan - to critically examine the history of the design process for Devanagari type.
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KUMAR, PRAJJWAL. "HANDWRITTEN CHARACTER RECOGNITION USING DEEP LEARNING." Thesis, 2022. http://dspace.dtu.ac.in:8080/jspui/handle/repository/19407.

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Handwritten character identification is a topic that has been researched for years and is an area of interest for the community of Pattern recognition researchers since It may be put to use in a wide range of fascinating applications. all across the field. This subject is a difficult challenge as a task because each person has their own unique writing style. SVM, ANN, and CNN models are some of the available options for handling this problem's many different ways and approaches. HCR is a need in the modern world since it assists us in a variety of fields of public domain, which makes it all the more vital to study in depth. Off-line digit recognition and online digit recognition are both examples of the hybrid character recognition (HCR) category. In this study, we review the many existing algorithms that have been implemented to get the better knowledge of the course, and we will come to a conclusion on the best strategies that are currently being developed for HCR. HCR for Devanagari is carried out by the performance of a computational device that accepts input from documents, screens, photos, and other responsive devices and believe to provides output by reading those images as an ASCII or UNICODE format. This theory is supported by the fact that computers have become increasingly powerful in recent years. Sanskrit, Nepali, Marathi, and Hindi are some of the languages that are represented in Devanagari. This script is a blend of numerous languages. This implementation is more important because the design of upper-case and lower-case characters in Devanagari are more complicated than in most other languages out there. Comparatively speaking, the set of characters and digits used in Devanagari is more complicated than the set of characters used in the English language. Character recognition has been hampered by the absence of verified datasets including Devanagari, which has made the task more difficult to do in the field.
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Bhargav, S. "Handwritten Devanagari numeral recognition." Thesis, 2014. http://ethesis.nitrkl.ac.in/6477/1/e-35.pdf.

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Optical character recognition (OCR) plays a very vital role in today’s modern world. OCR can be useful for solving many complex problems and thus making human’s job easier. In OCR we give a scanned digital image or handwritten text as the input to the system. OCR can be used in postal department for sorting of the mails and in other offices. Much work has been done for English alphabets but now a day’s Indian script is an active area of interest for the researchers. Devanagari is on such Indian script. Research is going on for the recognition of alphabets but much less concentration is given on numerals. Here an attempt was made for the recognition of Devanagari numerals. The main part of any OCR system is the feature extraction part because more the features extracted more is the accuracy. Here two methods were used for the process of feature extraction. One of the method was moment based method. There are many moment based methods but we have preferred the Tchebichef moment. Tchebichef moment was preferred because of its better image representation capability. The second method was based on the contour curvature. Contour is a very important boundary feature used for finding similarity between shapes. After the process of feature extraction, the extracted feature has to be classified and for the same Artificial Neural Network (ANN) was used. There are many classifier but we preferred ANN because it is easy to handle and less error prone and apart from that its accuracy is much higher compared to other classifier. The classification was done individually with the two extracted features and finally the features were cascaded to increase the accuracy.
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Sharma, Anand. "Devanagari Online Handwritten Character Recognition." Thesis, 2019. https://etd.iisc.ac.in/handle/2005/4633.

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In this thesis, a classifier based on local sub-unit level and global character level representations of a character, using stroke direction and order variations independent features, is developed for recognition of Devanagari online handwritten characters. It is shown that online character corresponding to Devanagari ideal character can be analyzed and uniquely represented in terms of homogeneous sub-structures called the sub-units. These sub-units can be extracted using direction property of online strokes in an ideal character. A method for extraction of sub-units from a handwritten character is developed, such that the extracted sub-units are similar to the sub-units of the corresponding ideal character. Features are developed that are independent of variations in order and direction of strokes in characters. The features are called histograms of points, orientations, and dynamics of orientations (HPOD) features. The method for extraction of these features spatially maps co-ordinates of points and orientations and dynamics of orientations of strokes at these points. Histograms of these mapped features are computed in di erent regions into which the spatial map is divided. HPOD features extracted from the sub-units represent the character locally; and those extracted from the character as a whole represent it globally. A classifier is developed that models handwritten characters in terms of the joint distribution of the local and global HPOD features of the characters and the number of sub-units in the characters. The classifier uses latent variables to model the structure of the the sub-units. The parameters of the model are estimated using the maximum likelihood method. The use of HPOD features and the assumption of independent generation of the sub-units given the number of sub-units, make the classifier independent of variations in the direction and order of strokes in characters. This sub-unit based classifier is called SUB classifier. Datasets for training and testing the classifiers consist of handwritten samples of Devanagari vowels, consonants, half consonants, nasalization sign, vowel omission sign, vowel signs, consonant with vowel sign, conjuncts, consonant clusters, and three more short strokes with di erent shapes. In all, there are 96 di erent characters or symbols that have been considered for recognition. The average number of samples per character class in the training and the test sets are, respectively, 133 and 29. The smallest and the largest dimensions of the extracted feature vectors are, respectively, 258 and 786. Since the size of the training set per class is not large compared to the dimension of the extracted feature vectors, the training set is small from the perspective of training any classifier. classifiers that can be trained on a small data set are considered for performance comparison with the developed classifier. Second order statistics (SOS), sub-space (SS), Fisher discriminant (FD), feedforward neural network (FNN), and support vector machines (SVM) are the other classifiers considered that are trained with the other features like spatio-temporal (ST), discrete Fourier transform (DFT), discrete cosine transform (DCT), discrete wavelet transform (DWT), spatial (SP), and histograms of oriented gradients (HOG) features extracted from the samples of the training set. These classifiers are tested with these features extracted from the samples of the test set. SVM classifier trained with DFT features has the highest accuracy of 90.2% among the accuracies of the other classifiers trained with the other features extracted from the test set. The accuracy of SUB classifier trained with HPOD features is 92.9% on the test set which is the highest among the accuracies of all the classifiers. The accuracies of the classifiers SOS, SS, FD, FNN, and SVM increase when trained with HPOD features. The accuracy of SVM classifier trained with HPOD features is 92.9%, which is the highest among the accuracies of the other classifiers trained with HPOD features. SUB classifier using HPOD features has the highest accuracy among the considered classifiers trained with the considered features on the same training set and tested on the same test set. The better character discriminative capability of the designed HPOD features is re ected by the increase in the accuracies of the other classifiers when trained with these features
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Kompalli, Suryaprakash. "A stochastic framework for font-independent Devanagari." 2007. http://proquest.umi.com/pqdweb?did=1273159701&sid=3&Fmt=2&clientId=39334&RQT=309&VName=PQD.

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Thesis (Ph.D.)--State University of New York at Buffalo, 2007.
Title from PDF title page (viewed on July 05, 2007) Available through UMI ProQuest Digital Dissertations. Thesis adviser: Govindaraju, Venu. Includes bibliographical references.
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Books on the topic "DEVANAGRI"

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(Firm), Suryastra. Devanagari. New Delhi: Suryastra, 2009.

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K, Qureshi M. H., ed. Aḥmad Farāz kī muntak̲h̲ab shāʻirī: Angrezī manẓūm tarjamah ke sāth = Selected poetry of Ahmad Faraz : Urdu, roman, Hindi (Devanagri) text with English lyrical translation. New Delhi: Star Publications, 2004.

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Mohan, Sarasvatī. Devanāgiri-lipi. San Jose: Sāndīpani, 1993.

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Miśra, Nareśa. Nāgarī lipi. Dillī: Nirmala Pablikeśansa, 1999.

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1923-, Tiwari Bholanath, ed. Hindī bhāshā kī lipi-saṃracanā. Dillī: Sāhitya Sahakāra, 1988.

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Dvivedī, Devīśaṅkara. Devanāgarī. Kurukshetra: Praśānta Prakāśana, Kurukshetra Viśvavidyālaya, 1990.

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Dvivedī, Devīśaṅkara. Devanāgarī. Kurukshetra: Praśānta Prakāśana, Kurukshetra Viśvavidyālaya, 1990.

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Ākabara, Caudhurī Golāma. Sileṭī Nāgarī parikramā. Sileṭa: Jālālābāda Lokasāhitya Parishada, 2002.

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Chandra, Lokesh, Vimala Gaṅgāprasāda, Bhāradvāja Rameśa, and Antarrāshṭrīya Nāgarī Lipi Sammelana (1st : 1999 : University of Delhi), eds. Bhāratīya bhāshāoṃ kī sahalipi Nāgarī. Dillī: Nāgarī Lipi Parishad, 1999.

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Saksenā, Rāma Prakāśa. Lipyantaraṇa: Siddhānta aura prayoga : Devanāgarī ke viśesha sandarbha meṃ : Hindī, Marāṭhī, Gujārātī, Bāṅgalā, Oṛiyā, Malayālama, Thāī, Urdū, Aṅgrezī. Rāyapura: Vaibhava Prakāśana, 2004.

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Book chapters on the topic "DEVANAGRI"

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Anand, Prakash, Piyush Ranjan, Priyanka Srivastava, and Deepak Kumar. "Hand-Written Devanagri Character Recognition Using Convolutional Neural Network in Python with Tensorflow." In Communications in Computer and Information Science, 122–37. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37303-9_10.

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Sharma, Diwakar, and Manu Sood. "Using a Technique Based on Moment of Inertia About an Axis for the Recognition of Handwritten Digit in Devanagri Script." In Communications in Computer and Information Science, 63–78. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07012-9_6.

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Sinha, Gita, and Shailja Sharma. "Offline Handwritten Devanagari Character Identification." In Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2019), 457–64. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43192-1_52.

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Shailesh Shah, Rutwik, Harshil Suresh Bhorawat, Hritik Ganesh Sawant, and Vinaya Sawant. "Word-Level Devanagari Text Recognition." In Practical Data Mining Techniques and Applications, 163–77. Boca Raton: Auerbach Publications, 2023. http://dx.doi.org/10.1201/9781003390220-12.

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Sukhadiya, Jeel, Yashi Suba, and Mitchell D’silva. "Devanagari Character Classification Using Capsule Network." In Advances in Intelligent Systems and Computing, 1040–49. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16657-1_97.

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Sharma, Ankit K., Dipak M. Adhyaru, and Tanish H. Zaveri. "A Survey on Devanagari Character Recognition." In Smart Systems and IoT: Innovations in Computing, 429–37. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8406-6_41.

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Kundaikar, Teja C., and J. A. Laxminarayana. "Efficient Recognition of Devanagari Handwritten Text." In Computational Intelligence in Data Mining - Volume 2, 81–88. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-2208-8_9.

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Halder, Chayan, Kishore Thakur, Santanu Phadikar, and Kaushik Roy. "Writer Identification from Handwritten Devanagari Script." In Advances in Intelligent Systems and Computing, 497–505. New Delhi: Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2247-7_51.

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Kavya, Addepalli, Nunna Vivek, Maddukuri Harika, and Venkatram Nidumolu. "Handwritten Devanagari Character Classification Using CNN." In Lecture Notes in Networks and Systems, 309–17. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7345-3_25.

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Dubey, Nidhi. "Digital Image Restoration of Historical Devanagari Manuscripts." In Advances in Intelligent Systems and Computing, 571–83. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1135-2_43.

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Conference papers on the topic "DEVANAGRI"

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Desai, Aarti, and Latesh Malik. "A modified approach to thinning of Devanagri characters." In 2011 3rd International Conference on Electronics Computer Technology (ICECT). IEEE, 2011. http://dx.doi.org/10.1109/icectech.2011.5941636.

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Malik, Latesh. "A Graph Based Approach for Handwritten Devanagri Word Recogntion." In 2012 5th International Conference on Emerging Trends in Engineering and Technology (ICETET). IEEE, 2012. http://dx.doi.org/10.1109/icetet.2012.42.

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Yadav, Nivedita, Santanu Chaudhury, and Prem Kalra. "Off-line skilled forgery detection on handwritten Devanagri script." In 2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG). IEEE, 2013. http://dx.doi.org/10.1109/ncvpripg.2013.6776172.

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Pachpande, Snehal, and Anagha Chaudhari. "Implementation of devanagri character recognition system through pattern recognition techniques." In 2017 International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2017. http://dx.doi.org/10.1109/icoei.2017.8300796.

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Shinde, Ambadas B., and Yogesh H. Dandawate. "Shirorekha extraction in Character Segmentation for printed devanagri text in Document Image Processing." In 2014 Annual IEEE India Conference (INDICON). IEEE, 2014. http://dx.doi.org/10.1109/indicon.2014.7030535.

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Dharsini, Ms S. Visnu, Narahari Kamath, Yash Chaudhary, Abhishek Sriramoju, and Nikhil Anurag. "Devanagri Character Image Recognition and Conversion into Text using Long Short Term Memory." In 2022 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI). IEEE, 2022. http://dx.doi.org/10.1109/icdsaai55433.2022.10028963.

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Kapil, Prashant, and Asif Ekbal. "A Transformer based Multi-Task Learning Approach Leveraging Translated and Transliterated Data to Hate Speech Detection in Hindi." In 3rd International Conference on Data Science and Machine Learning (DSML 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.121516.

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The increase in usage of the internet has also led to an increase in unsocial activities, hate speech is one of them. The increase in Hate speech over a few years has been one of the biggest problems and automated techniques need to be developed to detect it. This paper aims to use the eight publicly available Hindi datasets and explore different deep neural network techniques to detect aggression, hate, abuse, etc. We experimented on multilingual-bidirectional encoder representations from the transformer (M-BERT) and multilingual representations for Indian languages (MuRIL) in four settings (i) Single task learning (STL) framework. (ii) Transfering the encoder knowledge to the recurrent neural network (RNN). (iii) Multi-task learning (MTL) where eight Hindi datasets were jointly trained and (iv) pre-training the encoder with translated English tweets to Devanagari script and the same Devanagari scripts transliterated to romanized Hindi tweets and then fine-tuning it in MTL fashion. Experimental evaluation shows that cross-lingual information in MTL helps in improving the performance of all the datasets by a significant margin, hence outperforming the state-of-the-art approaches in terms of weightedF1 score. Qualitative and quantitative error analysis is also done to show the effects of the proposed approach.
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Mishra, Mayank, Tanupriya Choudhury, and Tanmay Sarkar. "Devanagari Handwritten Character Recognition." In 2021 IEEE India Council International Subsections Conference (INDISCON). IEEE, 2021. http://dx.doi.org/10.1109/indiscon53343.2021.9582192.

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Suryaprakash Kompalli, Sankalp Nayak, Srirangaraj Setlur, and Venu Govindaraju. "Challenges in OCR of Devanagari documents." In Eighth International Conference on Document Analysis and Recognition (ICDAR'05). IEEE, 2005. http://dx.doi.org/10.1109/icdar.2005.70.

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Narang, Vipin, Sujoy Roy, O. V. R. Murthy, and M. Hanmandlu. "Devanagari Character Recognition in Scene Images." In 2013 12th International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2013. http://dx.doi.org/10.1109/icdar.2013.184.

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