Topic
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.
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TL;DR: This work aims to develop a robust foreground/background segmentation(separation) technique that produces the highest recognition results in the scene text recognition process.
Abstract: Scene text recognition is a well-rooted research domain covering a diverse application area Recognition of scene text is challenging due to the complex nature of scene images Various structural characteristics of the script also influence the recognition process Text and background segmentation is a mandatory step in the scene text recognition process A text recognition system produces the most accurate results if the structural and contextual information is preserved by the segmentation technique Therefore, an attempt is made here to develop a robust foreground/background segmentation(separation) technique that produces the highest recognition results A ground-truth dataset containing Devanagari scene text images is prepared for the experimentation An encoder-decoder convolutional neural network model is used for text/background segmentation The model is trained with Devanagari scene text images for pixel-wise classification of text and background The segmented text is then recognized using an existing OCR engine (Tesseract) The word and character level recognition rates are computed and compared with other existing segmentation techniques to establish the effectiveness of the proposed technique
7 citations
02 Nov 2009
TL;DR: Though VKB starts with a higher user error rate compared to InScript, the error rate drops by 55% by the end of the experiment, and the input speed of VKB is found to be 81% higher than In Script, which points to interesting research directions for the use of multiple natural modalities for Indic text input.
Abstract: Multimodal systems, incorporating more natural input modalities like speech, hand gesture, facial expression etc., can make human-computer-interaction more intuitive by drawing inspiration from spontaneous human-human-interaction. We present here a multimodal input device for Indic scripts called the Voice Key Board (VKB) which offers a simpler and more intuitive method for input of Indic scripts. VKB exploits the syllabic nature of Indic language scripts and exploits the user's mental model of Indic scripts wherein a base consonant character is modified by different vowel ligatures to represent the actual syllabic character. We also present a user evaluation result for VKB comparing it with the most common input method for the Devanagari script, the InScript keyboard. The results indicate a strong user preference for VKB in terms of input speed and learnability. Though VKB starts with a higher user error rate compared to InScript, the error rate drops by 55% by the end of the experiment, and the input speed of VKB is found to be 81% higher than InScript. Our user study results point to interesting research directions for the use of multiple natural modalities for Indic text input.
7 citations
01 Jan 2010
TL;DR: A system which can transliterate any Hindi Unicode text to Urdu at 99.46% accuracy at word level is developed.
Abstract: In this paper, we present a high accuracy Hindi to Urdu transliteration system. Hindi and Urdu are variants of the same language, but while Hindi is written in the Devanagari script from left to right, Urdu is written in a script derived from a Persian modification of Arabic script written from right to left. To break this script barrier a Hindi-Urdu transliteration system has been developed. We have tried to overcome the shortcomings of the existing Hindi to Urdu transliteration systems and developed a system which can transliterate any Hindi Unicode text to Urdu at 99.46% accuracy at word level.
7 citations
TL;DR: The design and implementation of a machine transliteration system from Roman to Devanagari and vice-versa and the system produces outputs which are largely acceptable, however, pre-editing by way of insertion of phonetic symbols are required for error-free transliterations.
Abstract: Design and implementation of a machine transliteration system from Roman to Devanagari and vice-versa is presented. The Roman characters are grouped into 12 categories depending upon their phonetic correspondences with Devanagari. A state transition table is utilized to transform the Roman character string to a linearized Devanagari representation. Target Devanagari script is composed through a composition processor on a Devanagari terminal. The Devanagari to Roman conversion is primarily table driven, however, a number of heuristics are used to get a more acceptable form. The system produces outputs which are largely acceptable, however, pre-editing by way of insertion of phonetic symbols are required for error-free transliteration.
7 citations
05 Dec 2017
TL;DR: The proposed method is able to detect headlines in skewed word images and provides accurate result even when the headline is discontinuous or mostly absent, and is compared with a recent work to show the efficacy of the proposed methodology.
Abstract: Most segmentation algorithms for Indian scripts require some prior knowledge about the structure of a handwritten word to efficiently fragment the word into constituent characters. Zone detection is a considerably used strategy for this purpose. Headline estimation is a salient part of zone detection. In the present work, we propose a method that uses simple linear regression for estimating headlines present in handwritten words. This method efficiently detects headline in three Indian scripts, namely Bangla, Devanagari, and Gurmukhi. The proposed method is able to detect headlines in skewed word images and provides accurate result even when the headline is discontinuous or mostly absent. We have compared our method with a recent work to show the efficacy of our proposed methodology.
6 citations