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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.


Papers
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Book ChapterDOI
01 Jan 2015
TL;DR: Analysis of Devanagari characters for writer identification with 99.12 % accuracy for LIBLINEAR and LIBSVM classifiers of WEKA environment to get the individuality of characters.
Abstract: This paper presents analysis of Devanagari characters for writer identification. Being originated from Brahmic script, Devanagari is the most popular script in India. It is used by over 400 million people around the world. Application of writer identification of Devanagari handwritten characters covers a vast area such as The Questioned Document Examination (QDE) is an area of the Forensic Science with the main purpose to answer questions related to questioned document (authenticity, authorship and others). Signature verification in banking, in Graphology (study of handwriting) a theory or practice for inferring a person’s character, disposition, and attitudes from their handwriting. Here we collect 5 copies of handwritten characters to nullify intra-writing variation, from 50 different people mainly students. After preprocessing and character extraction, 64-dimensional feature is computed based on gradient of the images. Some manual processing is required because some noises are too difficult to remove automatically as they are much closer to the characters. We have used LIBLINEAR and LIBSVM classifiers of WEKA environment to get the individuality of characters. We have done the writer identification with all the characters and obtained 99.12 % accuracy for LIBLINEAR with all writers. Features collected from this work can be used in the next level to identify writers from their cursive writing.

15 citations

Proceedings ArticleDOI
24 Aug 2013
TL;DR: This work uses an ensemble of MLP classifiers having different hidden layer sizes and results of their classification are combined based on Adaboost technique, and studies use of boosting as a solution to this problem of using MLP as a classifier in real-life applications.
Abstract: In this article, we present our recent study of offline recognition of handwritten numerals of three Indian scripts -- Devanagari, Bangla and Oriya. Here, we propose a novel approach to combination of multiple MLP classifiers with varying number of hidden nodes based on Adaboost technique. In this recognition study, we used Zernike moment features of different orders. We obtained classification results corresponding to a number of orders of this moment function and the best classification result for each script was obtained when the feature vector consists of moment values up to the order 8. It is well-known that the classification performance of an MLP largely depends on the choice of the number of hidden nodes. In the present work, we studied use of boosting as a solution to this problem of using MLP as a classifier in real-life applications. Here, we use an ensemble of MLP classifiers having different hidden layer sizes and results of their classification are combined based on Adaboost technique. Classification results have been provided using publicly available databases [1] of offline handwritten numeral images of three Indian scripts.

15 citations

Proceedings ArticleDOI
01 Dec 2014
TL;DR: A novel approach for Devanagari text extraction from natural scene images using mathematical morphological operations to extract the headlines and the effectiveness of the adaptive thresholding approach was observed.
Abstract: In scenic images, information in the form of text provides vital clues for most applications based on image processing. These include assisted navigation content based image retrieval, automatic geocoding and understanding the scene. But in a multicolored complex background, it is quite a daunting task to locate the text. This task is daunting because of non-uniformity in illumination, complexity of the backdrop, and differences in the size font & line-orientation of the text. We propose a novel approach for Devanagari text extraction from natural scene images in this paper. We can use a text-to-speech engine or Optical Character Reader to recognize the extracted text. The basis of our scheme is to analyze the CCs. This is done to extract Devanagari text from scenic images captured by camera. The presence of head line is unique to this script. Our scheme makes use of mathematical morphological operations to extract the headlines. Also the binarization of scenic images was studied. Here the effectiveness of the adaptive thresholding approach was observed. The algorithm was tested on Devanagari text contained within a collection of 100 scenic images.

15 citations

Journal ArticleDOI
TL;DR: In this paper, robust methods for character segmentation and recognition for multilingual (Latin and Devanagari) Indian document images are presented, which are used for text segmentation.
Abstract: This paper presents robust methods for character segmentation and recognition for multilingual (Latin and Devanagari) Indian document images. The documents degraded over the years because of text d...

15 citations

Journal ArticleDOI
TL;DR: A database of 40 Devanagari character categories has been created from 200 pictures of the images in the wild to create a baseline for the comparison for the future works.
Abstract: papers examines the issues in recognizing the Devanagari characters in the wild like sign boards, advertisements, logos, shop names, notices, address posts etc. While some works deal with the issues in recognizing the machine printed and the handwritten Devanagari characters, it is not clear if such techniques can be directly applied to the Devanagari characters captured in the wild. Moreover in the recent times a lot of research has been conducted in the field of object categorization and localization. It would be interesting to investigate if the state%of%the%art tools for object categorization can also be applied to the recognition of the Devanagari characters. The idea is to view the isolated characters as objects so as to detect them in the wild. The ability to recognize the Devanagari characters in the wild will be very useful in the Internet services like Google street view and its associated applications. So, a detailed study of the Devanagari character recognition using the state%of %the%art character recognition and object recognition tools has been carried out to compute the best performance. This serve as a baseline for the comparison for the future works. There is no benchmark database to conduct studies on the Devanagari character recognition in the wild. So a database of 40 Devanagari character categories has been created from 200 pictures of the images in the wild.

15 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202342
202298
202148
202061
201938
201843