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.

Text detection of two major indian scripts in natural scene images

Abstract: In this article, we present a robust scheme for detection of Devanagari or Bangla texts in scene images These are the two most popular scripts in India The proposed scheme is primarily based on two major characteristics of such texts - (i) variations in stroke thickness for text components of a script are low compared to their non-text counterparts and (ii) presence of a headline along with a few vertical downward strokes originating from this headline We use the Euclidean distance transform to verify the general characteristics of texts in (i) Also, we apply the probabilistic Hough line transform to detect the characteristic headline of Devanagari and Bangla texts Further, similarity and adjacency measures are applied to identify text regions, which do not satisfy the verification in (ii) The proposed approach has been simulated on a repository of 120 images taken from Indian roads and the results are encouraging Also, we have discussed the applicability of the proposed scheme for detection of English texts Towards this end, we have considered the training and test samples from the image database of ICDAR 2003 Robust Reading Competition

Deep Convolutional Neural Network for Recognition of Unified Multi-Language Handwritten Numerals

Abstract: Deep learning systems have recently gained importance as the architecture of choice in artificial intelligence (AI). Handwritten numeral recognition is essential for the development of systems that can accurately recognize digits in different languages which is a challenging task due to variant writing styles. This is still an open area of research for developing an optimized Multilanguage writer independent technique for numerals. In this paper, we propose a deep learning architecture for the recognition of handwritten Multilanguage (mixed numerals belongs to multiple languages) numerals (Eastern Arabic, Persian, Devanagari, Urdu, Western Arabic). The overall accuracy of the combined Multilanguage database was 99.26% with a precision of 99.29% on average. The average accuracy of each individual language was found to be 99.322%. Results indicate that the proposed deep learning architecture produces better results compared to methods suggested in the previous literature.

Benchmark databases of handwritten Bangla-Roman and Devanagari-Roman mixed-script document images

Abstract: Handwritten document image dataset is one of the basic necessities to conduct research on developing Optical Character Recognition (OCR) systems. In a multilingual country like India, handwritten documents often contain more than one script, leading to complex pattern analysis problems. In this paper, we highlight two such situations where Devanagari and Bangla scripts, two most widely used scripts in Indian sub-continent, are individually used along with Roman script in documents. We address three key challenges here: 1) collection, compilation and organization of benchmark databases of images of 150 Bangla-Roman and 150 Devanagari-Roman mixed-script handwritten document pages respectively, 2) script-level annotation of 18931 Bangla words, 15528 Devanagari words and 10331 Roman words in those 300 document pages, and 3) development of a bi-script and tri-script word-level script identification module using Modified log-Gabor filter as feature extractor. The technique is statistically validated using multiple classifiers and it is found that Multi-Layer Perceptron (MLP) classifier performs the best. Average word-level script identification accuracies of 92.32%, 95.30% and 93.78% are achieved using 3-fold cross validation for Bangla-Roman, Devanagari-Roman and Bangla-Devanagari-Roman databases respectively. Both the mixed-script document databases along with the script-level annotations and 44790 extracted word images of the three aforementioned scripts are available freely at https://code.google.com/p/cmaterdb/ .