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.

Approach for Printed Devanagari Script Recognition

Abstract: ant con­ juncts and consonant-vowel combinations take different forms based on their position in the word. We also in­ troduce a new database, Deva-DB, of Devanagari script (free of cost) to aid the research towards a robust De­ vanagari OCR system. On this database, LSTM-based OCRopus system yields error rates ranging from 1.2% to 9.0% depending upon the complexity of the training and test data. Comparison with open-source Tesseract system is also presented for the same database.

A Survey of Feature Extraction Methods for Handwritten Devnagari Character Recognition

Abstract: In India, Devanagari script is used by more than 300 million people. There has been a significant improvement in the research related to the recognition of handwritten Devanagari characters in the past few years. But accurate handwritten character recognition is a difficult task due to variations in shapes of the same character with different writer. Selection of feature extraction is the most important factor in achieving high recognition performance in Optical Character Recognition (OCR) systems. This paper presents review on different feature extraction methods for off-line handwritten Devnagari OCR. The feature extraction methods are discussed based on Statistical, Structural, and Global transformation and moments. Along with feature-extraction methods Preprocessing, Segmentation, and Classification techniques useful for the recognition are discussed in various sections of the paper. An attempt is made to address the recognition rates of these feature extraction methods and finally research scope in the Devnagari OCR is discussed. Moreover, the paper also contains an ample bibliography of many selected papers as an aid for the researchers working in the field of Devanagari OCR.

Towards Accurate Identification and Removal of Shirorekha from Off-line Handwritten Devanagari word Documents

Abstract: Shirorekha identification and removal is an important and a challenging pre-processing stage in almost all machine interpretations for handwritten Devanagari documents. Within this area of investigation, all studies are designed based on traditional image processing techniques. Which are mainly based on hand-engineering and learn local transformations only. However, it can also be viewed as a supervised classification task in which each pixel, in a document, is examined/ queried so that those classified as shirorekha are removed. For this purpose, we extended this area of investigation by designing an encoder-decoder based convolutional neural network (EDCNN). Which have demonstrated, from various studies, that they learn image intricacies very well. The contribution of this work is three-fold, first, we created our own handwritten word dataset comprising of words with and without shirorekha, such that, effective training takes place. Next, we trained the proposed network with binary as well as in gray scale formats. Finally, we demonstrated that the proposed approach is accurate and generalizable.

Recognition of Online Handwritten Bangla and Devanagari Basic Characters: A Transfer Learning Approach

Abstract: The transfer learning approach has eradicated the need for running the Convolutional Neural Network (CNN) models from scratch by using a pre-trained model with pre-set weights and biases for recognition of different complex patterns. Going by the recent trend, in this work, we have explored the transfer learning approach to recognize online handwritten Bangla and Devanagari basic characters. The transfer learning models considered here are VGG-16, ResNet50, and Inception-V3. To impose some external challenges to the models, we have augmented the training datasets by adding different complexities to the input data. We have also trained these three transfer learning models from scratch (i.e., not using pre-set weights of the pre-trained models) for the same recognition tasks. Besides, we have compared the outcomes of both the procedures (i.e., running from scratch and by using pre-trained models). Results obtained by the models are promising, thereby establishing its effectiveness in developing a comprehensive online handwriting recognition system.

Performance Analysis of Thinning Algorithms for Offline-Handwritten Devanagari Words

Abstract: AbstractThinning or skeletonize is useful preprocessing step in pattern recognition systems such as character or word recognition system. In past few decades, various thinning algorithms are reported in literature for such type of systems to make them more reliable, independent of font variations and efficient. In this paper, performance of three thinning algorithms developed by Zhang-Suen [ZSu], Guo-Hall [GHa] and Lee-Kashyab-Chu [LKC] has been analyzed to check their suitability to skeletonize handwritten Devanagari words in terms of various objective (reduction rate, sensitivity measurement and thinness measurement) and subjective (mean opinion score) performance metrics. For the present work, the performance these algorithms has been tested using a handwritten Devanagari words database having 15-word-classes, collected from hundreds of writers. It has been observed that [LKC] thinning algorithm achieved higher reduction rate, thinness measurement and mean opinion score as compared with [ZSu] and [GHa] algorithms which show its more-suitability to skeletonize handwritten Devanagari words. Moreover, slightly-higher value of sensitivity measurement also depicts that resultant skeleton may contain some artifacts, redundant branches and lines caused by noise.KeywordsThinningSkeletonFeature extractionClassificationHandwritten word recognition