Devanagari

About: Devanagari is a research topic. Over the lifetime, 655 publications have been published within this topic receiving 7428 citations. The topic is also known as: Deva nagari & Hindi Script.

Handwritten Devanagari numeral recognition

Abstract: 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.

Developing a Devanagari-based multi-language orthography for Nepalese languages

Abstract: 1. Background This working paper presents an analysis of the existing practice of using Devanagari for the languages of Nepal with the list of advantages and disadvantages, and a proposal for a Devanagari-based multi-language orthography for the languages of Nepal. The paper is divided into seven sections including background.

Dynamic Phrase Generation for Detection of Idioms of Gujarati Language using Diacritics and Suffix-based Rules

Abstract: Gujarati is the language used for everyday communication in the state of Gujarat, India. The Gujarati language is also officially recognized by the constitution and the government of India. Gujarati script is based on the Devanagari script. An idiom is an expression, phrase, or word that has a different meaning from the literal meaning of the words in it. Idioms represent the cultural heritage of Gujarati language. Idioms are used in Gujarati language for effective communication and convey of an accurate message. No Machine Translation System does the accurate translation of Gujarati idioms to English or any other language. Different idiom phrases can be generated by adding diacritic(s) as well as suffix to the root or base form of the idiom. Many forms of single idiom make automatic idiom identification as well as machine translation more challenging. This paper focuses on the design and implementation of diacritics and suffix-based rules for dynamic phrase generation and detection of idioms of Gujarati language. This implementation helps in identifying Gujarati idiom present in any possible form in the Gujarati text. The obtained results with the execution of 7050 different Gujarati idiom phrases yield an accuracy of 99.73%. The results are encouraging enough to make the proposed implementation useful for Natural Language processing tasks related to Gujarati language idioms.

An Artificial Neural Network based Multistage Offline Handwritten Character Recognition

Abstract: Devanagari is one of the basic Script widely used for many Indian Languages Like Hindi, Marathi, Rajasthani etc. Devanagari Scripts Hindi language is the third common language used all over the word. In this work we propose an artificial neural network based classifier and statistical and structural method based feature extraction. Optical isolated Hindi Characters are taken as an input image through the scanner. An input image is preprocessed and is segmented in terms of various structural and stastical features like End points, middle bar, loop, end bar, aspect ratio. Features are extracted and the feature vector is applied to Self organizing map (SOM) which is one of the classifier of an artificial neural Network. SOM is trained for such 500 different characters collected from 500 persons. The characters are classified into three different classes. The proposed classifier attains 91% accuracy.