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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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01 Jan 2011
TL;DR: A methodology for off-line Isolated handwritten Devanagari character recognition using three feature extraction techniques based on recursive subdivisions of the character image so that the resulting sub-images at each iteration have balanced numbers of foreground pixels.
Abstract: In this paper, we present a methodology for off-line Isolated handwritten Devanagari character recognition The proposed methodology relies on a three feature extraction techniques The first technique is based on recursive subdivisions of the character image so that the resulting sub-images at each iteration have balanced (approximately equal) numbers of foreground pixels, as far as this is possible Second technique is based on the zone density of the pixel and third is based on the directional distribution of neighboring background pixels to foreground pixels The 314 sized feature vector is form from the three feature extraction techniques for a handwritten Devanagari character The dataset (12240 samples) of handwritten Devanagari Character, have been prepared by writing the different - 2 people who belongs to different age group and obtained the 9489 % recognition accuracy

12 citations

Proceedings Article
11 Mar 2015
TL;DR: This paper presents an attempt to solve the challenging problem of Devanagari numeral and character recognition with Multilayer perceptron neural network (MLP-NN) and shows results that show 93.17% recognition accuracy using 40 hidden neurons for numerals and 82.7% recognition accurate using 60hidden neurons for characters.
Abstract: This paper presents an attempt to solve the challenging problem of Devanagari numeral and character recognition. It uses structural and geometric features to represent the Devanagari numerals and characters. Each image is zoned in 9 blocks and 8 structural features are extracted from each block. Similarly 9 global geometric features are extracted. These 81 features are used for representing the image. Multilayer perceptron neural network (MLP-NN) is used for classification. 3000 handwritten samples of Devanagari numerals and 5375 handwritten samples of Devanagari alphabetic characters are used for training and testing. Experimental results show 93.17% recognition accuracy using 40 hidden neurons for numerals and 82.7% recognition accuracy using 60 hidden neurons for characters. Fivefold cross validation is used for verifying the results.

12 citations

Journal ArticleDOI
TL;DR: In this paper , a Convolutional Neural Network (CNN) was used for digitization of Devanagari handwritten text recognition (DHTR) using 46 classes of characters and each class has two thousand different images.

12 citations

Proceedings ArticleDOI
24 Aug 2013
TL;DR: An algorithm for extraction and recognition of Bangla and Devanagari text form video frames with complex background by using Adaptive SIS binarization technique and state of the art OCR.
Abstract: Extraction and recognition of Bangla text from video frame images is challenging due to fonts type and style variation, complex color background, low-resolution, low contrast etc. In this paper, we propose an algorithm for extraction and recognition of Bangla and Devanagari text form video frames with complex background. Here, a two-step approach has been proposed. After text localization, the text line is segmented into words using information based on line contours. First order gradient values of the text blocks are used to find the word gap. Next, an Adaptive SIS binarization technique is applied on each word. Next this binarized text block is sent to a state of the art OCR for recognition.

12 citations

Proceedings ArticleDOI
09 Jul 2015
TL;DR: An automatic HSI technique for document images of six popular Indic scripts namely Bangla, Devanagari, Malayalam, Oriya, Roman and Urdu, using a GAS (Greedy Attribute Selection) method is proposed.
Abstract: In a multi-script country like India, prior identification of script from document images is an essential step before choosing appropriate script specific OCR The problem becomes more complex and challenging in case of HSI (Handwritten Script Identification) An automatic HSI technique for document images of six popular Indic scripts namely Bangla, Devanagari, Malayalam, Oriya, Roman and Urdu is proposed in this paper A Block-level approach is followed for the same and initially 34-dimensional feature vector is constructed applying transform based (BRT, BDCT, BFFT and BDT), textural and statistical techniques Finally using a GAS (Greedy Attribute Selection) method 20 attributes are selected for learning process Total 600 unconstrained document image blocks of size 512×512 each, are prepared with equal distribution of each script type The whole dataset is divided into 2:1 ratio for training and testing Extensive experimentation is carried out for Six-scripts, Tetra-scripts, Tri-scripts and Bi-scripts combinations Experimental result shows promising and comparable performance

12 citations


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