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.

Akkhara-Muni: An Instance for Classifying PALI Characters

Abstract: Handwritten Recognition and Archaeology are significant facts of antediluvian epoch and scripts which are not easy to learn. For example, Pali written in Sinhala, Khmer, Burmese, Devanagari, Lao and many more which are having prodigious influence in the Buddhism culture since they hoard the lessons. Discussions in this paper portray need of ancient scripts to be recognized. Recognition of Akkhara-Muni, which constitutes Pali alphabet, is presented in this paper, process has a few steps trailed by OCR for classification with the results showing classification accuracy of 85.66%.

A Study on the Effect of Outliers in Devanagari Character Recognition

Abstract: Devanagari is the basic script for many languages of India, including their National language Hindi. Unlike the Latin script used for the English language, it does not have upper case or lowercase. It has only one case of writing. Moreover each alphabet contains more curves than straight lines. Hence handwritten Devanagari character recognition is a challenging task. To capture different handwritten styles of each alphabet, different approaches have been proposed. In this work, we investigate a simple filtering technique on the features. Support Vector Machine (SVM) was used as classifier. It has been applied on two benchmark Devanagari databases and results show an improvement of as much as 5-10%. This improvement is found to be consistent with different sizes of the database. It was studied on pixel density features and GIST features separately. GIST features were found to be more effective and like having the potency of self-containing filtering.

Stop word detection in compressed textual images: An experiment on indic script documents

Abstract: Stop word detection is attempted in this work in the context of retrieval of document images in the compressed domain. Algorithms are presented to identify text lines and words and to cluster similar words to count word occurrence frequencies. A list of words with their occurrence frequencies is generated from a corpus of textual images. As stop words in any language show high occurrence frequencies, such words occupy the upper positions in the sorted word list. Experiments have been carried out on two major indic scripts (Devanagari (Hindi) and Bangla). Test results using 150 document images consisting of about 12 K words in each script show the promising potential of the proposed approach.

A multiscript gaze-based assistive virtual keyboard

Abstract: The recent development of inexpensive and accurate eye-trackers allows the creation of gazed based virtual keyboards that can be used by a large population of disabled people in developing countries. Thanks to eye-tracking technology, gaze-based virtual keyboards can be designed in relation to constraints related to the gaze detection accuracy and the considered display device. In this paper, we propose a new multimodal multiscript gaze-based virtual keyboard where it is possible to change the layout of the graphical user interface in relation to the script. Traditionally, virtual keyboards are assessed for a single language (e.g. English). We propose a multiscript gaze based virtual keyboard that can be accessed for people who communicate with the Latin, Bangla, and/or Devanagari scripts. We evaluate the performance of the virtual keyboard with two main groups of participants: 28 people who can communicate with both Bangla and English, and 24 people who can communicate with both Devanagari and English. The performance is assessed in relation to the information transfer rate when participants had to spell a sentence using their gaze for pointing to the command, and a dedicated mouth switch for commands selection. The results support the conclusion that the system is efficient, with no difference in terms of information transfer rate between Bangla and Devanagari. However, the performance is higher with English, despite the fact it was the secondary language of the participants.