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.

The poisonous potency of script: Hindi and Urdu : Digraphia: writing systems and society

Abstract: Hindi and Urdu are variants of the same language characterized by extreme digraphia: Hindi is written in the Devanagari script from left to right, Urdu in a script derived from a Persian modification of Arabic script written from right to left. High variants of Hindi look to Sanskrit for inspiration and linguistic enrichment, high variants of Urdu to Persian and Arabic. Hindi and Urdu diverge from each other cumulatively, mostly in vocabulary, as one moves from the bazaar to the higher realms, and in their highest - and therefore most artificial - forms the two languages are mutually incomprehensible. The battle between Hindi and Urdu, the graphemic conflict in particular, was a major flash point of Hindu/Muslim animosity before the partition of British India into India and Pakistan in 1947.

Design and evaluation of Devanagari virtual keyboards for touch screen mobile phones

Abstract: Lack of an easy and efficient text input mechanism in Indic scripts has been a barrier to large-scale adoption of ICTs in India. We present findings from a usability evaluation of three keyboard designs for Indic scripts for touch screen phones. The design of one of the keyboards is based on the frequency of characters, while the designs of the other two are based on the logical structure of the script. We evaluated the keyboards with participants with low-level of education through a first-time usability test and a longitudinal usability test. One of the logically structured keyboards started out with significantly higher success rate, typing speed, and lesser errors than the other two. The longitudinal test involving text input of 500 words did not conclusively prove that either design was better. Our study establishes benchmarks for text input speeds, errors and ratings for the initial learning phase for text input in Marathi among less educated users.

Offline handwritten devanagari script segmentation

Abstract: The process of segmentation is a vital part in any script/character recognition technique. Devanagari is mostly useful Script in India for number of officials and banking applications. Segmentation of Devanagari script is difficult because of presence of large character set which include vowels, consonants, compound characters and modifiers. This paper focus on the line, word, character segmentation of handwritten Devanagari script for efficient script recognition.

Kannada, Telugu and Devanagari Handwritten Numeral Recognition with Probabilistic Neural Network: A Novel Approach

Abstract: In this paper, a novel approach for Kannada, Telugu and Devanagari handwritten numerals recognition based on global and local structural features is proposed. Probabilistic Neural Network (PNN) Classifier is used to classify the Kannada, Telugu and Devanagari numerals separately. Algorithm is validated with Kannada, Telugu and Devanagari numerals dataset by setting various radial values of PNN classifier under different experimental setup. The experimental results obtained are encouraging and comparable with other methods found in literature survey. The novelty of the proposed method is free from thinning and size normalization. General Terms Pattern Recognition, Document image processing.

Zernike moment feature extraction for handwritten Devanagari compound character recognition

Abstract: Compound character recognition of Devanagari script is one of the challenging tasks since the characters are complex in structure and can be modified by writing combination of two or more characters. These compound characters occurs 12 to 15% in the Devanagari Script. The moment based techniques are being successfully applied to several image processing problems and represents a fundamental tool to generate feature descriptors where the Zernike moment technique has a rotation invariance property which found to be desirable for handwritten character recognition. This paper discusses extraction of features from handwritten compound characters using Zernike moment feature descriptor and proposes SVM and k-NN based classification system. The proposed classification system preprocess and normalize the 27000 handwritten character images into 30×30 pixels images and divides them into zones. The pre-classification produces three classes depending on presence or absence of vertical bar. Further Zernike moment feature extraction is performed on each zone. The overall recognition rate of proposed system using SVM and k-NN classifier is upto 98.37%, and 95.82% respectively.