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.

Printed and Handwritten Character & Number Recognition of Devanagari Script using Gradient Features

Abstract: In this paper we are extracting feature of handwritten and ISM printed characters of devanagri script. we are extracting Gradient feature of the devanagari script ,for that we are using two operators i.e. Sobel and Robert operator respectively . Here we are computing gradient in 8,12,16,32 directions and getting different feature vectors respectively. We are using each directional vector separately for classification.

Handwritten Devanagari Word Recognition: A Curvelet Transform Based Approach

Abstract: This paper presents a new offline handwritten Devanagari word recognition system. Though Devanagari is the script for Hindi, which is the official language of India, its character and word recognition pose great challenges due to large variety of symbols and their proximity in appearance. In order to extract features which can distinguish similar appearing words, we employ Curvelet Transform. The resultant large dimensional feature space is handled by careful application of Principal Component Analysis (PCA). The Support Vector Machine (SVM) and k-NN classifiers were used with one-against-rest class model. Results of Curvelet feature extractor and classifiers have shown that Curvelet with k-NN gave overall better results than the SVM classifier and shown highest results (93.21%) accuracy on a Devanagari handwritten words set.

Towards Spotting and Recognition of Handwritten Words in Indic Scripts

Abstract: Handwriting recognition (HWR) in Indic scripts is a challenging problem due to the inherent subtleties in the scripts, cursive nature of the handwriting and similar shape of the characters. Lack of publicly available handwriting datasets in Indic scripts has affected the development of handwritten word recognizers, and made direct comparisons across different methods an impossible task in the field. In this paper, we propose a framework for annotating large scale of handwritten word images with ease and speed. We also release a new handwritten word dataset for Telugu, which is collected and annotated using the proposed framework. We also benchmark major Indic scripts such as Devanagari, Bangla and Telugu for the tasks of word spotting and handwriting recognition using state of the art deep neural architectures. Finally, we evaluate the proposed pipeline on RoyDB, a public dataset, and achieve significant reduction in error rates.

Kannada, Telugu and Devanagari Handwritten Numeral Recognition with Probabilistic Neural Network: A Script Independent Approach

Abstract: In this paper a script independent automatic numeral recognition system is proposed. A single algorithm is proposed for recognition of Kannada, Telugu and Devanagari handwritten numerals. In general the number of classes for numeral recognition system for a scripts/language is 10. Here, three scripts are considered for numeral recognition forming 30 classes. In the proposed method 30 classes have been reduced to 18 classes. The global and local structural features like directional density estimation, water reservoirs, maximum profile distances and fill hole density are extracted. A Probabilistic neural network (PNN) classifier is used in the recognition system. The algorithms efficiency is for various radial values of PNN classifiers, with different experimental setup and obtained encouraging results are compared to other methods proposed in the literature survey. A total of 2550 numeral images of Kannada, Telugu and Devanagari scripts are considered for experimentation. The overall accuracy of the system is 97.20%. The novelty of the proposed method is that, it is script independent, thinning free, fast, and without size normalization.