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.

Characterizing Hand Written Devanagari Characters using Evolved Regular Expressions

Abstract: Devanagari script is used in the Indian subcontinent for several major languages such as Hindi, Sanskrit, Marathi and Nepali languages. More than 500 million people use the script. Recognition of unconstrained (Handwritten) Devanagari writing is more complex than English cursive due to shape of constituent strokes. The method that has been proposed is using segmentation and evolved regular expressions. It has been taken care into account that there is vast variation in writing styles size and thickness of characters and any distortion during scanning. There is no need of preprocessing as well as training. The notation of regular expression is short and precise and can be easily transformed into directed graphs or finite - state automata accepting all the symbol strings generated by the corresponding expressions. The method is efficient enough because of power of regular expressions

Devanagari character recognition using structure analysis

Abstract: A method of character recognition using prior knowledge of the script is proposed Devanagari, a script widely used in India at present, and found in Buddhist texts of the past, is used for this purpose This study is confined to recognizing a particular font used in a printed Buddhist text: Saddharmapundarika >

Devanagari Ancient Character Recognition using HOG and DCT Features

Abstract: In the present work, a system for recognition of ancient documents in Devanagari script is presented. Two feature extraction techniques, namely, DCT(Discrete Cosine Transformation) zigzag features and Histogram of oriented gradients are considered for extracting features of Devanagari ancient manuscripts. For recognition, three classification techniques, namely, SVM (Support Vector Machine), decision tree, and Naive Bayes are used. A database for the experiments is collected from various libraries and museums. Using SVM classifier with RBF kernel, a recognition accuracy of 90.70% with DCT zigzag feature vector of length 100 has been reported. A recognition accuracy of 90.70% with a partitioning strategy of dataset (80% data as training data and the remaining 20% data as testing data) has been achieved.

Handwritten Devanagari Compound Character Recognition Using Legendre Moment: An Artificial Neural Network Approach

Abstract: Handwritten Devanagari Compound character recognition is one of the new challenging task for the researcher, because Compound character are complex in structure, they are written by combination two or more character. Their occurrence in the script is up to 12 to 15%. In this research paper, a recognition system for handwritten Devanagari Compound Character is proposed bases on Legendre moment feature descriptor are used to recognize. Moment function have been successfully applied to many pattern recognition problem, due to this they tends to capture global features which makes them well suited as feature descriptor. The process image is normalized to 30X30 pixel size divided into zone, from this structural as well as statistical feature are extracted from each zone. The proposed system is trained and tested on 27000 handwritten collected from different people. For classification we have used Artificial Neural Network. The overall recognition rate for basic is up to 98.25% and for all compound character is 98.36%.

Multi-script handwriting recognition with FOHDEL

Abstract: Until now handwritten character recognition systems used script specific methodologies. We present a unified syntactic approach for multi-script recognition. The fuzzy pattern description language FOHDEL is used to store fuzzy features in the form of fuzzy rules. First we briefly describe the proposed recognition methodology for Latin, Devanagari and Kanji scripts by analyzing their characteristic properties. Further we present the main features of FOHDEL by a comparative rule generation of three scripts under experiment. Finally the system integration of the proposed multi-script recognition scheme in the existing Latin recognition system is presented.