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.

Shirorekha extraction in Character Segmentation for printed devanagri text in Document Image Processing

Abstract: Finding Structural Layout, Text Line Segmentation, Word Level Segmentation and Character Level Segmentation is major step in offline OCR systems for Devanagari Script in Document Image Processing. This paper proposes a Word and Character Segmentation method for machine printed Devanagari text. A complete word and character segmentation system for Devanagari printed text is presented here. Sometimes, interline space and fused characters make line segmentation and character segmentation a difficult task respectively. We have tested our method on documents in Marathi scripts. A novel technique of character segmentation for printed Devanagari text is presented here. After removing the Shirorekha (header line) of Devanagari text, the bounding boxes are used to surround the segmented characters. Results obtained from this method are encouraging because of morphological operations. In this method we are proposing some basic morphological operations on the scanned document images and got much better results.

An OCR based approach for word spotting in Devanagari documents

Abstract: This paper describes an OCR-based technique for word spotting in Devanagari printed documents. The system accepts a Devanagari word as input and returns a sequence of word images that are ranked according to their similarity with the input query. The methodology involves line and word separation, pre-processing document words, word recognition using OCR and similarity matching. We demonstrate a Block Adjacency Graph (BAG) based document cleanup in the pre-processing phase. During word recognition, multiple recognition hypotheses are generated for each document word using a font-independent Devanagari OCR. The similarity matching phase uses a cost based model to match the word input by a user and the OCR results. Experiments are conducted on document images from the publicly available ILT and Million Book Project dataset. The technique achieves an average precision of 80% for 10 queries and 67% for 20 queries for a set of 64 documents containing 5780 word images. The paper also presents a comparison of our method with template-based word spotting techniques.

Augmented Handwritten Devanagari Digit Recognition Using Convolutional Autoencoder

Abstract: Handwritten digit recognition has turned into one of the demanding areas of research in the field of image processing. Many approaches have been proposed which include a statistical method, fuzzy technique, and neural network for feature classification and feature selection but have not been found to use convolutional autoencoder for Devanagari digit after performing image augmentation on the training dataset. This paper shows the use of unsupervised training using convolutional autoencoder with deep ConvNet in order to detect handwritten Devanagari digits, i.e., 0–9. Convolutional autoencoder is the type of autoencoder that is used to encode the input for extracting important features and then try to reconstruct the input image. This paper shows the improved accuracy of Hindi, English and Bangla digit dataset by using the proposed approach and also performing a number of cross-validation experiments on all three datasets using image augmentation.

Brief review of research on Devanagari script

Abstract: In this paper we have done a brief survey of Devanagari script, research and different classifiers approach used for Character Recognition .We have collected and created database of handwritten characters. We have preprocessed it, extracted feature using local intensity distribution of gradient and used in our experiment. We have used KNN Classifier. Experiment result illustrates the accuracy, rejection, error percentage of classification. Keywords- Devanagari script, KNN Classifier, accuracy, rejection, error percentage of classification

Deep Learning Approach for Devanagari Script Recognition

Abstract: In this paper, we have proposed a new technique for recognition of handwritten Devanagari Script using deep learning architecture. In any OCR or classification system extracting discriminating feature is most important and crucial step for its success. Accuracy of such system often depends on the good feature representation. Deciding upon the appropriate features for classification system is highly subjective and requires lot of experience to decide proper set of features for a given classification system. For handwritten Devanagari characters it is very difficult to decide on optimal set of good feature to get good recognition rate. These methods use raw pixel values as features. Deep Learning architectures learn hierarchies of features. In this work, first image is preprocessed to remove noise, converted to binary image, resized to fixed size of 30×40 and then convert to gray scale image using mask operation, it blurs the edges of the images. Then we learn features using an unsupervised stacked Restrict...