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.

Effective UI Design to Explore Poetry Literature Using Multiple Scripts for Ordinary End Users

Abstract: India is a multilingual, multi-script country having more than one natural languages, castes, and religions. Each religion has their saint’s, who had written literature for advancing humanity for being human either in the form prose or poetry. This literature is available on the Internet in the form of poems, abhangas, verses, and shlokas in Indian languages especially in Hindi and Marathi languages along with English. To search the literature through websites and mobile applications, require the knowledge of local languages along with English. It is noticed that there are no helpful UI provided by websites to explore these literature as compared to commercial websites for ordinary end users. Therefore, this work focuses to conduct the study of available websites to look over their UI, global and local search facility for Indian saints and poets literature between twelfth and twentieth centuries from ordinary end users point of view. After knowing the key requirements of ordinary end users, this study further extended from web based to online and offline mobile applications with searching interface to access the literature written in Devanagari script supporting Hindi and Marathi languages as by default search is always in Roman script. The outcome of study provides data with subjective satisfaction to user.

An Ancient Number Recognition using Freeman Chain Code with Deep Learning Approach

Abstract: Sanskrit character and number documents have a lot of errors. Correcting those errors using conventional spell-checking approaches breaks down due to the limited vocabulary. This is because of high inflexions of Sanskrit, where words are dynamically formed by Sandhi rules, Samasa rules, Taddhita affixes, etc. Therefore, correcting OCR documents require huge efforts. Here, we can present different machine learning approaches and various ways to improve features for ameliorating the error corrections in Sanskrit documents. Simulation of Sanskrit dictionary for synthesizing off-the-shelf dictionary can be done. Most of the proposed methods can also work for general Sanskrit word corrections and Hindi word corrections. Handwriting recognition in Indic scripts, like Devanagari, is very challenging due to the subtitles in the scripts, variations in rendering and the cursive nature of the handwriting. Lack of public handwriting datasets in Indic scripts has long stymied the development of offline handwritten word recognizers and made comparison across different methods a tedious task in the field. In this paper, a new handwritten word dataset will be released for Devanagari, IIIT-HW-Dev to alleviate some of these issues. This process is required for successful training of deep learning architecture, availability of huge amounts of training data is crucial, as any typical architecture contains millions of parameters. A new method for the classification of freeman chain code using four-connectivity and eight-connectivity events with deep learning approach is presented. Application of CNN LeNet-5 is found to be suitable to get results in this cases as the numbers are formed with curved lines In contrast with the existing FCC event data analysis techniques, sampled grey images of the existing events are not used, but image files of the three-phase PQ event data are analysed by taking the advantage of the success of the deep learning approach on imagefile-classification. Therefore, the novelty of the proposed approach is that image files of the voltage waveforms of the three phases of the power grid are classified. It is shown that the test data can be classified with 100% accuracy. The proposed work is believed to serve the needs of the future smart grid applications, which are fast and taking automatic countermeasures against potential PQ events.