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.

Segmentation of devanagari-script handwriting for recognition

Abstract: Methods and systems for recognizing Devanagari script handwriting are provided. A method may include receiving a handwritten input and determining that the handwritten input comprises a shirorekha stroke based on one or more shirorekha detection criteria. Shirorekha detection criteria may be at least one criterion such as a length of the shirorekha stroke, a horizontality of the shirorekha stroke, a straightness of the shirorekha stroke, a position in time at which the shirorekha stroke is made in relation to one or more other strokes in the handwritten input, and the like. Next, one or more recognized characters may be provided corresponding to the handwritten input.

Skew Detection and Correction of Gurmukhi Words from Natural Scene Images

Abstract: Natural scene images are more susceptible to skew deformation as compared to document text which makes skew correction an indispensable step in scene text extraction. This work evaluates Murthy’s Devanagari scene word slant correction method [Signal, Image and Video Processing, 7(6), 2012] on Gurmukhi scene images. The method makes use of headline feature of Devanagari which also exist in Gurmukhi script. The headline of Gurmukhi word is found by perceiving farthest located salient points as its end-points and skew angle of headline is calculated from its slope. Gurmukhi word image is deskewed using skew angle of identified headline. The method has been tested on 100 selfcaptured good quality Gurmukhi and 117 publically available Devanagari scene words with average accuracy of 62.8% and 72.2% respectively. The method has been found to be working well on few samples of defective scene words, provided actual end-points of headline are preserved. It has been observed that Murthy’s method is very simple to implement, does not require any pre-processing and give good results in wide variety of practical situations. However, this method does not work well for single character words with vowel above headline and words with identified headline parallel to horizontal axis.

Recent Adaptions of the Devanagari Script for the Tibeto-Burman Languages of Nepal

Abstract: In this paper, the author is concerned primarily with two problems, specifically with respect to the 100+ Tibeto-Burman languages of Nepal and the attempts to write these languages with the Devanagari script. It is not attempt to survey all these languages; rather, he reports on six which have only recently come to be written with the Devanagari script: Chantyal, Gurung, Limbu, Sherpa, Tamang, and Thangmi. These languages constitute a rough-and-ready sample of the Tibeto-Burman languages of Nepal without a tradition of writing in the Devanagari script: some are locally prominent languages with large numbers of speakers, others more obscure; some have traditions of writing in scripts other than Devanagari, others do not. For none of these languages is there a fully standardized orthography, and none of these languages has been written in the Devanagari script in any serious way until quite recently. The goal of this paper is to examine the ways in which the Devanagari script has, and has not, been adapted to the phonologies of the six languages. The author first discusses the orthographic histories of each language, then the characteristics of the Devanagari script and the phonological system it is designed to accommodate. Later he presents brief sketches of the phonological inventories of the six languages, following by a discussion of how the script has — and has not — been adapted to accommodate these sound systems. The last section discusses the issue of ‘superfluous graph-emes’, and finish up with some concluding remarks on how Devanagari has been — and could be — adapted for these languages.

Handwritten Devanagari Character Recognition

Abstract: Recognition of Devanagari character consists of Image correction, segmentation and character recognition. Image correction digitizes the input characters making it available for further processing. Principle component analysis is used to discover the hidden and unclear part and segmentation separates individual characters to identify each character. The most crucial part of any character recognition system is the process of segmentation as characters are recognized individually. The result of recognition is dependent on the accuracy of segmentation. For extraction and recognition we used Eigen space method which uses Gerschgorin's theorem for comparison. Handwritten Devanagari script is nowadays a popular topic for researchers as less work is done on this topic. Handwritten Devanagari characters are difficult to recognize due to the presence of header line and various modifiers. Recognition of fused characters is also a major concern for researchers as fused character is treated as a single character resulting in an error.

Multi-font Devanagari Text Recognition Using LSTM Neural Networks

Abstract: Current research in OCR is focusing on the effect of multi-font and multi-size text on OCR accuracy. To the best of our knowledge, no study has been carried out to study the effect of multi-fonts and multi-size text on the accuracy of Devanagari OCRs. The most popular Devanagari OCRs in the market today are Tesseract OCR, Indsenz OCR and eAksharayan OCR. In this research work, we have studied the effect of font styles, namely Nakula, Baloo, Dekko, Biryani and Aparajita on these three OCRs. It has been observed that the accuracy of the Devanagari OCRs is dependent on the type of font style in text document images. Hence, we have proposed a multi-font Devanagari OCR (MFD_OCR), text line recognition model using long short-term memory (LSTM) neural networks. We have created training dataset Multi_Font_Train, which consists of text document images and its corresponding text file. This consists of each text line in five different font styles, namely Nakula, Baloo, Dekko, Biryani and Aparajita. The test dataset is created using the text from benchmark dataset [1] for each of the font styles as mentioned above, and they are named as BMT_Nakula, BMT_Baloo, BMT_Dekko, BMT_Biryani and BMT_Aparajita test dataset. On the evaluation of all OCRs, the MFD_OCR showed consistent accuracy across all these test datasets. It obtained comparatively good accuracy for BMT_Dekko and BMT_Biryani test datasets. On performing detailed error analysis, we noticed that compared to other Devanagari OCRs, the MFD_OCR has consistent, insertion and deletion type of errors, across all test dataset for each font style. The deletion errors are negligible, ranging from 0.8 to 1.4%.