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Статті в журналах з теми "DEVANAGRI"

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Автор: Grafiati

Опубліковано: 11 вересня 2023

Оновлено: 16 квітня 2024

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

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Анотація:

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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2

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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3

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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4

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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5

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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6

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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7

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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Анотація:

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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8

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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9

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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10

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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11

Khatri, Suman, and Irphan Ali. "Hindi Numeral Recognition using Neural Network." International Journal of Advance Research and Innovation 1, no. 3 (2013): 29–39. http://dx.doi.org/10.51976/ijari.131304.

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Handwriting has continued to persist as a means of communication and recording information in day-to-day life even with the introduction of new technologies. The constant development of computer tools lead to the requirement of easier interface between the man and the computer. Handwritten character recognition may for instance be applied to Zip-Code recognition, automatic printed form acquisition, or cheques reading. The importance to these applications has led to intense research for several years in the field of off-line handwritten character recognition. „Hindi‟ the national language of India (written in Devanagri script) is world‟s third most popular language after Chinese and English. Hindi handwritten character recognition has got lot of application in different fields like postal address reading, cheques reading electronically. Recognition of handwritten Hindi characters by computer machine is complicated task as compared to typed characters, which can be easily recognized by the computer. This paper presents a scheme to recognize Hindi number numeral with the help of neural network.

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12

Rani, Vneeta, and Dr Vijay Laxmi. "Segmentation of Handwritten Text Document Written in Devanagri Script for Simple character, skewed character and broken character." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 8, no. 1 (June 20, 2013): 686–91. http://dx.doi.org/10.24297/ijct.v8i1.3427.

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OCR (optical character recognition) is a technology that is commonly used for recognizing patterns artificial intelligence & computer machine. With the help of OCR we can convert scanned document into editable documents which can be further used in various research areas. In this paper, we are presenting a character segmentation technique that can segment simple characters, skewed characters as well as broken characters. Character segmentation is very important phase in any OCR process because output of this phase will be served as input to various other phase like character recognition phase etc. If there is some problem in character segmentation phase then recognition of the corresponding character is very difficult or nearly impossible.

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13

Parthasarathy, Arpitha. "The Spiritual Form of Ancient Art and Culture - Bharatanatyam (Visual Art) Depicted Using Unique Techniques on Scratchboard (Fine Art) Medium." Journal of Arts and Humanities 6, no. 3 (March 15, 2017): 33. http://dx.doi.org/10.18533/journal.v6i3.1143.

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<p>The most ancient form of dance that is prevailing todays is a form of classical Indian dance, Bharatanatyam. In Sanskrit (and Devanagri), bharatanatyam means "Indian dance", is believed to have divine origin and is of the most ancient form of classical dance. Bharatanatyam is a two thousand-year-old dance form, originally practiced in the temples of ancient India. The art today remains purely devotional even today and this performing art is yet to gain awareness and interest in the western world. This dance form has various implications in improving the higher order thinking in children and provides health benefits in adults apart from cultural preservation. The current study uses scratchboard as a medium to display the artistic movements and emotions. Scratchboard, a fine art is one means by which the visual art is expressed in this current study using sharp tools, namely X-acto 11 scalpel and tattoo needles. This unique medium made up of a masonite hardboard coated with soft clay and Indian ink has been used to not only show the details of the ancient dance form and expression but also to comprehend and transcribe both visual art and fine art. It is for the first time that scratchboard medium has been the innovatively used to show various textures of flower, glistening gold jewels, hand woven silk and the divine expression in the same art ‘devotion’. The current study was carried out in-order to perpetuate, conserve and disseminate these classic forms of visual art and fine art.</p>

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14

Ahmad, Rizwan. "Urdu in Devanagari: Shifting orthographic practices and Muslim identity in Delhi." Language in Society 40, no. 3 (May 24, 2011): 259–84. http://dx.doi.org/10.1017/s0047404511000182.

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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)*

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15

Yadav, Bharati, Ajay Indian, and Gaurav Meena. "HDevChaRNet: A deep learning-based model for recognizing offline handwritten devanagari characters." Journal of Autonomous Intelligence 6, no. 2 (August 15, 2023): 679. http://dx.doi.org/10.32629/jai.v6i2.679.

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<p>Optical character recognition (OCR) converts text images into machine-readable text. Due to the non-availability of several standard datasets of Devanagari characters, researchers have used many techniques for developing an OCR system with varying recognition rates using their own created datasets. The main objective of our proposed study is to improve the recognition rate by analyzing the effect of using batch normalization (BN) instead of dropout in convolutional neural network (CNN) architecture. So, a CNN-based model HDevChaRNet (Handwritten Devanagari Character Recognition Network) is proposed in this study for same to recognize offline handwritten Devanagari characters using a dataset named Devanagari handwritten character dataset (DHCD). DHCD comprises a total of 46 classes of characters, out of which 36 are consonants, and 10 are numerals. The proposed models based on convolutional neural network (CNN) with BN for recognizing the Devanagari characters showed an improved accuracy of 98.75%, 99.70%, and 99.17% for 36, 10, and 46 classes, respectively.</p>

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16

Deshpande, Mr Onkar. "Postal Address Identification and Sorting." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 4946–53. http://dx.doi.org/10.22214/ijraset.2021.36023.

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In this fast-moving world, a normal man can take considerable time to find a postal card in a bunch of postcards with significant issues like unclear handwriting, having trouble recognizing some uncommon or ambiguous names. Also, in postal offices or industries, it negatively impacts the efficiency of the postal system. I am making a system for Indian postal automation based on recognizing pin-code on the postcard. In India, there are multiple languages were speak. Indian postcards are mainly written in three languages the state's official language, English, and Devanagari language. In India, more than 50% of people write Pincode digits in either English or Devanagari language, so I am making such a system that sorts both English and Devanagari language postcards. Moreover, the system is mature enough to recognize handwritten as well as printed digits. As a result, the system gets an accuracy of 92.59% on the English language postcards, 90% accuracy on the Devanagari language postcards e and the digit recognition model gives accuracy 99.23% Devanagari numerals and 99.43% accuracy on English numerals.

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17

PANDEY, Krishna Kumar, and Smita JHA. "Tracing the Identity and Ascertaining the Nature of Brahmi-derived Devanagari Script." Acta Linguistica Asiatica 9, no. 1 (January 30, 2019): 59–73. http://dx.doi.org/10.4312/ala.9.1.59-73.

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Анотація:

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.

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18

Padmaja, Kannuru. "Devanagari Handwritten Character Recognition Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (January 31, 2022): 102–5. http://dx.doi.org/10.22214/ijraset.2022.39744.

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Abstract: In this paper, we present the implementation of Devanagari handwritten character recognition using deep learning. Hand written character recognition gaining more importance due to its major contribution in automation system. Devanagari script is one of various languages script in India. It consists of 12 vowels and 36 consonants. Here we implemented the deep learning model to recognize the characters. The character recognition mainly five steps: pre-processing, segmentation, feature extraction, prediction, post-processing. The model will use convolutional neural network to train the model and image processing techniques to use the character recognition and predict the accuracy of rcognition. Keywords: convolutional neural network, character recognition, Devanagari script, deep learning.

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19

Susanti, Catur Rahayu Ning, Sugiarti Sugiarti, and Gede Merthawan. "PEMBELAJARAN AKSARA DEVANAGARI PADA SISWA HINDU DI SDN 2 TATURA PALU." Widya Genitri : Jurnal Ilmiah Pendidikan, Agama dan Kebudayaan Hindu 12, no. 2 (December 30, 2021): 131–48. http://dx.doi.org/10.36417/widyagenitri.v12i2.390.

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Анотація:

Realitas yang terjadi adalah aksara Devanagari yang seharusnya dapat diperoleh anak sejak dini justru tidak diajarkan sama sekali di ketiga jenjang pendidikan yang ada (formal, informal dan non formal). Hal ini menyebabkan sebagian besar anak terkadang merasa asing dan bingung ketika pertama kali melihat kitab-kitab suci agama Hindu. Tujuan dari penelitian ini adalah untuk memperoleh gambaran pembelajaran aksara Devanagari pada siswa Hindu dan juga kendala yang dihadapi serta upaya yang dilakukan selama pembelajaran aksara Devanagari berlangsung. Jenis penelitian ini adalah dekriptif kualitatif. Teori yang digunakan adalah teori belajar kognitif dan teori belajar konstruktivisme. Penentuan sumber data menggunakan purposive sampling dengan metode pengumpulan data melalui observasi, wawancara, dokumentasi dan studi kepustakaan. Sedangkan teknik analisis data yang digunakan adalah reduksi data, penyajian data, penarikan kesimpulan.

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20

More, Vijay, and Madan Kharat. "Segmentation of Lines and Words of Handwritten Devanagari Text using Connected Components with Statistics Method." Journal of Scientific Research 66, no. 02 (2022): 179–88. http://dx.doi.org/10.37398/jsr.2022.660224.

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The pre-processing activities for handwritten Devanagari text recognition includes an significant step called Segmentation. The segmentation accuracy of Devanagari text characters depends entirely on the accurately segmented lines and words in the handwritten documents. The process of segmenting lines and words correctly leads to many issues. More detailed information is lagging on the segmentation of lines and words from Devanagari text documents, whereas it is available more for other script documents in the literature. Here, we accomplished the task of segmenting the lines and words using Connected Components with Statistics Method on PHDIndic_11 dataset. Experimentation using above mentioned method resulted in line segmentation accuracy of 91.91% and word segmentation accuracy of 72.89% which outperforms over Global threshold and Otsu’s optimum threshold methods.

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21

Sawant, Sakshi S., Aparna S. Shirkande, Neha V. Shinde, and Sharanya S. Rao. "OCR Of Devanagari Script Using CNN." Journal of Optical Communication Electronics 9, no. 2 (May 23, 2023): 1–10. http://dx.doi.org/10.46610/jooce.2023.v09i02.001.

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Анотація:

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%.

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22

Singh, Mitali, Pranava Gayathri, and Prabhakar Kandukuri. "OCR for devanagari script." MATEC Web of Conferences 392 (2024): 01128. http://dx.doi.org/10.1051/matecconf/202439201128.

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The introduction of Optical Character Recognition (OCR) technology revolutionized text digitization, allowing physical documents to be converted into editable and searchable digital representations. This paper goes into the unique challenges and advancements in OCR designed exclusively for the Devanagari script. It helps to preserve cultural heritage by digitizing historic manuscripts and religious writings and making them more accessible. Furthermore, Devanagari OCR has practical uses in administrative activities, data entry, and educational content digitization.

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23

Mohinder Kumar, Sanjeev Kumar. "Devanagari CAPTCHA: For the Security in Web." Tuijin Jishu/Journal of Propulsion Technology 44, no. 4 (October 17, 2023): 292–310. http://dx.doi.org/10.52783/tjjpt.v44.i4.837.

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Анотація:

Completely Automated Public Turing Test to Tell Computers and Humans Apart or CAPTCHA is a solution for cyber-attack. CAPTCHA is a small challenge that an internet user has to pass before accessing any online service. The most common type of CAPTCHA is text-based CAPTCHA, in which a small image (contains a random number of alphabets) is presented before the user. The user has to identify and then type the alphabet in a text box. The textual information in the CAPTCHA must not be identified by a bot (computer code). So, artificial noise and distortion are applied in the image. Earlier text-based schemes use English alphabets, but over time non-English language-based text CAPTCHAs also came into the picture. Native language-based text CAPTCHA is very useful for internet users who do not know the English language. This article is an effort towards the current status of the Devanagari script-based CAPTCHAs. We have analyzed 28 unique Devanagari CAPTCHAs from a security and usability point of view. Total 28000 different samples are collected for this experiment. For the success of a text-based CAPTCHA, it must be very secure from the bot and easy for human beings. Devanagari CAPTCHA can be very beneficial for Indian websites. This paper is written by keeping the importance of Devanagari script-based CAPTCHA.

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24

Naik, Dr Vishal, and Heli Mehta. "Comparison of Various Algorithms for Handwritten Character Recognition of Indian Languages." International Journal for Research in Applied Science and Engineering Technology 11, no. 10 (October 31, 2023): 696–703. http://dx.doi.org/10.22214/ijraset.2023.56079.

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Abstract: In this paper, we present a comparison of various pre-processor, feature extraction methods and algorithms for handwritten character recognition of various Indian languages. Comparison of classifier, feature set and accuracy of offline handwritten character recognition of Gujarati, Devanagari, Gurmukhi, Kannada, Malayalam, Bangla and Hindi Indian languages. Comparison of classifier, feature set and accuracy of online handwritten character recognition of Assamese, Tamil, Devanagari, Malayalam, Gurmukhi, and Bangla Indian languages. Indian language wise best performance of each language is compared for both offline and online handwritten character recognition systems.

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25

Deore, Shalaka P. "DHCR_SmartNet: A smart Devanagari Handwritten Character Recognition using Level-wised CNN Architecture." Computer Science 23, no. 3 (October 2, 2022): 303. http://dx.doi.org/10.7494/csci.2022.23.3.4487.

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Handwritten Script Recognition is a vital application of Machine Learning domain. Applications like automatic number plate detection, pin code detection and managing historical documents increasing more attention towards handwritten script recognition. English is the most widely spoken language, hence there has been a lot of research into identifying a script using a machine. Devanagari is popular script used by a huge number of people in the Indian Subcontinent. In this paper, level-wised efficient transfer learning approach presented on VGG16 model of Convolutional Neural Network (CNN) for identification of Devanagari isolated handwritten characters. In this work a new dataset of Devanagari characters is presented and made accessible publicly. Newly created dataset comprises 5800 samples for 12 vowels, 36 consonants and 10 digits. Initially simple CNN is implemented and trained on this new small dataset. In next stage transfer learning approach is implemented on VGG16 model and in last stage fine-tuned efficient VGG16 model is implemented. The training and testing accuracy of fine-tuned model are obtained as 98.16% and 96.47% respectively.

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26

Susan, Seba, and Jatin Malhotra. "Recognising Devanagari Script by Deep Structure Learning of Image Quadrants." DESIDOC Journal of Library & Information Technology 40, no. 05 (November 4, 2020): 268–71. http://dx.doi.org/10.14429/djlit.40.05.16336.

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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.

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Shreekanth, T., and V. Udayashankara. "A Histogram-Based Two-Stage Adaptive Character Segmentation for Transcription of Inter-Point Hindi Braille to Text." International Journal of Image and Graphics 15, no. 03 (June 11, 2015): 1550012. http://dx.doi.org/10.1142/s0219467815500126.

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Problem statement: The optical Braille character recognition (OBR) system is in substantial need in order to preserve the Braille documents to make them available in future for the large section of visually impaired people. The recognition and transcribing of the double sided Braille document into its corresponding natural text is indeed a challenging task. This difficulty is due to the overlapping of the front side dots (recto) with that of the back side dots (verso) in the inter-point Braille document. In such settings, the habitual method of template matching to distinguish recto and verso dots is unproductive. Approach: A fresh system for double sided Braille dot recognition is proposed, which employs a two-stage highly efficient and an adaptive technique to differentiate the recto and verso dots from an inter-point Braille expending the horizontal and vertical projection profiles along with distance thresholding for Braille character segmentation. Materials: The efficacy of this segmentation technique is demonstrated on a large dataset consisting of Hindi Devanagari Braille documents with varying image resolution and with diverse word patterns. The primary reason for choosing the Hindi Devanagari Braille is that, Hindi is the national language of India and OBR for the Hindi Devanagari Braille is not available. Results: Braille line segmentation accuracy of 100%, word segmentation accuracy of 99.8% and character segmentation accuracy of 99.4% has been accomplished. Conclusion: This effort of OBR development for Hindi Devanagari Braille has been done for the first time. The proposed method is tolerant to merging of Braille dots and presence of half characters.

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Agnihotri, Ved Prakash. "Offline Handwritten Devanagari Script Recognition." International Journal of Information Technology and Computer Science 4, no. 8 (July 16, 2012): 37–42. http://dx.doi.org/10.5815/ijitcs.2012.08.04.

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

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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

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Srivastav, Ankita, and Neha Sahu. "Segmentation of Devanagari Handwritten Characters." International Journal of Computer Applications 142, no. 14 (May 18, 2016): 15–18. http://dx.doi.org/10.5120/ijca2016909994.

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V. Ramana Murthy, O., and M. Hanmandlu. "Zoning based Devanagari Character Recognition." International Journal of Computer Applications 27, no. 4 (August 31, 2011): 21–25. http://dx.doi.org/10.5120/3289-4481.

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., Ashok Kumar Bathla, and Sunil Kumar Gupta . "Character Segmentation and Skew Correction for Handwritten Devanagari Scripts: A Friends Technique." Asian Journal of Engineering and Applied Technology 8, no. 1 (February 5, 2019): 50–54. http://dx.doi.org/10.51983/ajeat-2019.8.1.1060.

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Optical Character Recognition (OCR) technology allows a computer to “read” text (both typed and handwritten) the way a human brain does.Significant research efforts have been put in the area of Optical Character Segmentation (OCR) of typewritten text in various languages, however very few efforts have been put on the segmentation and skew correction of handwritten text written in Devanagari which is a scripting language of Hindi. This paper aims a novel technique for segmentation and skew correction of hand written Devanagari text. It shows the accuracy of 91% and takes less than one second to segment a particular handwritten word.

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Moharkar, Lalita, Sudhanshu Varun, Apurva Patil, and Abhishek Pal. "A scene perception system for visually impaired based on object detection and classification using CNN." ITM Web of Conferences 32 (2020): 03039. http://dx.doi.org/10.1051/itmconf/20203203039.

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In this paper we have developed a system for visually impaired people using OCR and machine learning. Optical Character Recognition is an automated data entry tool. To convert handwritten, typed or printed text into data that can be edited on a computer, OCR software is used. The paper documents are scanned on simple systems with an image scanner. Then, the OCR program looks at the image and compares letter shapes to stored letter images. OCR in English has evolved over the course of half a century to a point that we have established application that can seamlessly recognize English text. This may not be the case for Indian languages, as they are much more complex in structure and computation compared to English. Therefore, creating an OCR that can execute Indian languages as suitably as it does for English becomes a must. Devanagari is one of the Indian languages spoken by more than 70% of people in Maharashtra, so some attention should be given to studying ancient scripts and literature. The main goal is to develop a Devanagari character recognition system that can be implemented in the Devanagari script to recognize different characters, as well as some words.

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Singh, Pratibha, Ajay Verma, and Narendra S. Chaudhari. "On the Performance Improvement of Devanagari Handwritten Character Recognition." Applied Computational Intelligence and Soft Computing 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/193868.

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The paper is about the application of mini minibatch stochastic gradient descent (SGD) based learning applied to Multilayer Perceptron in the domain of isolated Devanagari handwritten character/numeral recognition. This technique reduces the variance in the estimate of the gradient and often makes better use of the hierarchical memory organization in modern computers.L2-weight decay is added on minibatch SGD to avoid overfitting. The experiments are conducted firstly on the direct pixel intensity values as features. After that, the experiments are performed on the proposed flexible zone based gradient feature extraction algorithm. The results are promising on most of the standard dataset of Devanagari characters/numerals.

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Deore, Shalaka Prasad. "Human Behavior Identification Based on Graphology Using Artificial Neural Network." Acadlore Transactions on AI and Machine Learning 1, no. 2 (December 31, 2022): 101–8. http://dx.doi.org/10.56578/ataiml010204.

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Handwriting reflects a person's true nature, phobias, emotional outbursts, honesty, defenses and many more characteristics. Analysis of handwriting, also known as graphology, is a science that uses the strokes and patterns disclosed by handwriting to identify, evaluate, and analyze personality. It is the study of the patterns and physical characteristics of handwriting to identify the author, indicate the author's psychological state while writing, or analyze personality traits. Traditionally, professionals also called graphologists predict the behavior of the writer by analyzing their handwriting, but this procedure is tedious and expensive. Therefore, this paper focuses on developing an application for personality identification that can predict behavioral characteristics directly using a computer without any human involvement. Most of the existing applications use English as the primary language to identify the personality trait of the writer however, our approach uses Devanagari scripts for prediction, thereby eliminating the language barrier. Our proposed method uses a machine learning approach to predict personality by analyzing Devanagari samples using Artificial Neural Network. We have created our own Devanagari word dataset. There are almost 4000 images which belong to 5 classes namely Introvert, Extrovert, Optimistic, Pessimistic and Stable mind-set. The testing accuracy achieved by the proposed method is 94.75%.

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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.

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Mani, Sahil. "Devanagari Character Recognition using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 7, no. 3 (March 31, 2019): 2660–63. http://dx.doi.org/10.22214/ijraset.2019.3485.

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Indira, B., Muhammad Shuaib Qureshi, Mahaboob Sharief Shaik, Rashad Mahmood Saqib, and M. V Ramana Murthy. "Devanagari Character Recognition: A Short Review." International Journal of Computer Applications 59, no. 6 (December 18, 2012): 23–27. http://dx.doi.org/10.5120/9553-4011.

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Kumar,, Gaurav, and Sachin Kumar. "CNN Based Handwritten Devanagari Digits Recognition." International Journal of Computer Sciences and Engineering 5, no. 7 (July 2017): 71–74. http://dx.doi.org/10.26438/ijcse/v5i7.7174.

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V.RamanaMurthy, O., Sujoy Roy, Vipin Narang, and M. Hanmandlu. "Devanagari Character Recognition in the Wild." International Journal of Computer Applications 38, no. 4 (January 28, 2012): 38–45. http://dx.doi.org/10.5120/4599-6800.

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Narang, Sonika Rani, M. K. Jindal, and Munish Kumar. "Line Segmentation of Devanagari Ancient Manuscripts." Proceedings of the National Academy of Sciences, India Section A: Physical Sciences 90, no. 4 (June 20, 2019): 717–24. http://dx.doi.org/10.1007/s40010-019-00627-2.

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42

Kumar, Mohinder, Manish Kumar Jindal, and Munish Kumar. "A Novel Attack on Monochrome and Greyscale Devanagari CAPTCHAs." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 4 (May 26, 2021): 1–30. http://dx.doi.org/10.1145/3439798.

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The use of computer programs in breaching web site security is common today. CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) and human interaction proofs are the cost-effective solution to these kinds of computer attacks on web sites. These CAPTCHAs are available in many forms, such as those based on text, images and audio. A CAPTCHA must be secure enough that it cannot be broken by a computer program, and it must be usable enough that humans can easily understand it. The most popular is the text-based scheme. Most text-based CAPTCHAs are based on the English language and are not usable by the native people of India. Research has proven that native people are more comfortable with native language–based CAPTCHA. Devanagari-based CAPTCHAs are also available, but the security aspect has not been tested. Unfortunately, English language–based CAPTCHAs are successfully broken. Therefore, it is important to test the security of Devanagari script-based CAPTCHAs. We picked five unique monochrome CAPTCHAs and five unique greyscale CAPTCHAs for testing security. We achieved 88.13% to 97.6% segmentation rates on these schemes and generated six types of features for these segmented characters, such as raw pixels, zoning, projection, Scale-Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF) and Oriented Fast and Rotated BRIEF (ORB). For classification, we used three classifiers for comparative analyses. Using k-Nearest Neighbour (k-NN), Support Vector Machine (SVM) and Random Forest, we achieved high recognition on monochrome and greyscale schemes. For monochrome Devanagari CAPTCHAs, the recognition rate of k-NN ranges from 64.78% to 82.39%, SVM ranges from 76.46% to 91.34% and Random Forest ranges from 80.34% to 91.28%. For greyscale Devanagari CAPTCHAs, the recognition rate of k-NN ranges from 67.52% to 85.47%, SVM ranges from 76.9% to 91.71% and Random Forest ranges from 83.07% to 92.13%. We achieved a breaking rate for monochrome schemes of 66% to 85% and for greyscale schemes of 73% to 93%.

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Prabhanjan, S., and R. Dinesh. "Deep Learning Approach for Devanagari Script Recognition." International Journal of Image and Graphics 17, no. 03 (July 2017): 1750016. http://dx.doi.org/10.1142/s0219467817500164.

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In this paper, we have proposed a new technique for recognition of handwritten Devanagari Script using deep learning architecture. In any OCR or classification system extracting discriminating feature is most important and crucial step for its success. Accuracy of such system often depends on the good feature representation. Deciding upon the appropriate features for classification system is highly subjective and requires lot of experience to decide proper set of features for a given classification system. For handwritten Devanagari characters it is very difficult to decide on optimal set of good feature to get good recognition rate. These methods use raw pixel values as features. Deep Learning architectures learn hierarchies of features. In this work, first image is preprocessed to remove noise, converted to binary image, resized to fixed size of 30[Formula: see text][Formula: see text][Formula: see text]40 and then convert to gray scale image using mask operation, it blurs the edges of the images. Then we learn features using an unsupervised stacked Restricted Boltzmann Machines (RBM) and use it with the deep belief network for recognition. Finally network weight parameters are fine tuned by supervised back propagation learning to improve the overall recognition performance. The proposed method has been tested on large set of handwritten numerical, character, vowel modifiers and compound characters and experimental results reveals that unsupervised method yields very good accuracy of (83.44%) and upon fine tuning of network parameters with supervised learning yields accuracy of (91.81%) which is the best results reported so far for handwritten Devanagari characters.

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Singh, NandiniChatterjee, and Chaitra Rao. "Reading in Devanagari: Insights from functional neuroimaging." Indian Journal of Radiology and Imaging 24, no. 1 (2014): 44. http://dx.doi.org/10.4103/0971-3026.130691.

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Shiravale, Sankirti Sandeep, R. Jayadevan, and Sanjeev S. Sannakki. "Devanagari Text Detection From Natural Scene Images." International Journal of Computer Vision and Image Processing 10, no. 3 (July 2020): 44–59. http://dx.doi.org/10.4018/ijcvip.2020070104.

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Text present in a camera captured scene images is semantically rich and can be used for image understanding. Automatic detection, extraction, and recognition of text are crucial in image understanding applications. Text detection from natural scene images is a tedious task due to complex background, uneven light conditions, multi-coloured and multi-sized font. Two techniques, namely ‘edge detection' and ‘colour-based clustering', are combined in this paper to detect text in scene images. Region properties are used for elimination of falsely generated annotations. A dataset of 1250 images is created and used for experimentation. Experimental results show that the combined approach performs better than the individual approaches.

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Sawant, U. M., R. K. Parkar, S. L. Shitole, and S. P. Deore. "Devanagari Script Recognition using Capsule Neural Network." International Journal of Computer Sciences and Engineering 7, no. 1 (January 31, 2019): 208–11. http://dx.doi.org/10.26438/ijcse/v7i1.208211.

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Sinha, Gita, Shailja Sharma, and Ashif Habibi. "Review Paper Devanagari and Gurumukhi Character Recognition." International Journal of Computer Sciences and Engineering 7, no. 4 (April 30, 2019): 653–57. http://dx.doi.org/10.26438/ijcse/v7i4.653657.

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Khare, Sonal, and Jaiveer Singh. "Handwritten Devanagari Character Recognition System: A Review." International Journal of Computer Applications 121, no. 9 (July 18, 2015): 10–14. http://dx.doi.org/10.5120/21566-4600.

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Jangid Kartar Singh, Mahesh, Renu Dhir, and Rajneesh Rani. "Performance Comparison of Devanagari Handwritten Numerals Recognition." International Journal of Computer Applications 22, no. 1 (May 31, 2011): 1–6. http://dx.doi.org/10.5120/2551-3496.

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A. Dongare, Ms Seema, Prof Dhananjay B. Kshirsagar, and Ms Snehal V. Waghchaure. "Handwritten Devanagari Character Recognition using Neural Network." IOSR Journal of Computer Engineering 16, no. 2 (2014): 74–79. http://dx.doi.org/10.9790/0661-162107479.

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