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.

Ariyaka: A PALI Alphabet Recognition Script

Abstract: Despite the fact that handwriting recognition is playing an essential part in to days era as well as learning of deep-rooted writings which belongs to reign of Budhha and resembles to ancient scripts and artefacts that are not easy to grasp and holds our culture base. PALI is prop for every language used around 1 4 century B.C. And is written in numerous languages or scripts resembling Sinhala, Khmer, Burmese, Devanagari, Lao and many more which are having remarkable influence. Discussions in this paper displays need of ancient scripts to be recognized and Ariyaka is taken as base script here which is recognized by Modified OCR classification with an accuracy of 85.73%.

A Brief Study of Feature Extraction and Classification Methods Used for Character Recognition of Brahmi Northern Indian Scripts

Abstract: According to the 8th schedule of Indian constitution, there are 22 official languages and 122 regional languages prevalent in India. In the last few decades, the recognition of these scripts has been prominent area of research. Among these scripts most of the recognition research work has been done for Bangla, Devanagari, Gujrati, Gurumukhi and Telugu scripts etc. Commercial OCRs were available for various scripts like Latin, Japanese, Chinese, Roman, Arabic scripts. OCR systems for few Indian scripts are available and others are in the stage of development for preserving manuscripts and ancient literatures written in different Indian scripts and making digital libraries for the documents. Further, overall accuracy of the recognition, feature extraction and classification are crucial phases. This paper attempts to give a brief summary of various feature extraction and classification methods used for recognition process of Brahmi Northern Indian scripts by the researchers in last few decades.

The Performance Evaluation of Deep Learning Classifier to Recognize Devanagari Handwritten Characters and Numerical

Abstract: 1 Research scholar, Dept. of CS & IT, M.J.P. Rohilkhand University, Bareilly (U.P.), India 2 Professor, Dept. of CS & IT, M.J.P. Rohilkhand University, Bareilly (U.P.), India 1 anuj2k3@gmail.com, 2 rsiet2002@gmail.com Abstract A text classification is a well formed process using various measurable properties and computerized logical procedure to fetch a pattern from different classes.Since classification is important for the pattern recognition process, there are some issues with well-formed classification in this process, which is one of the important issues for proper development and improvement of productive data examinations. On behalf of the versatility of learning and the ability to deal with complex calculations, classifiers are consistently best suited for design patter recognition issues. The aim of this paper is to present a result based comparative study of different classifiers and the optimal recognition of results computation through the Devanagari Handwritten characters and numerical values. Different classifiers were used and evaluated in this investigation including k-Nearest Neighbor (k-NN), Support-Vector machine (SVM), Naïve Bayes, Decision Tree, Random Forest, and Convolution Neural Network (CNN). To accomplish the experiment purpose, this paper used an unbiased dataset with including 123 samples that consists of 123 characters and 123 numerical values. Python 3.0 with sciket learn machine learning open-source environment library have been used to evaluate the performance of the classifiers. The performances of the classifiers accessed by considering the different matrices including dataset volume with best split ratio among training, validation, and testing process, accuracy rate, Ture/False acceptance rate, True/False rejection rate and the area covered under the receiver operating characteristic curve. Similarly the paper shows the correlation of the accuracy of the experiments obtained by applying to chosen the classifier. On behalf of the exploratory results, the infallible classifiers considered in this test have free rewards and must be executed in a hybrid manner to meet the thigh precision rates.In the views of test work, their result compressions and the examination to be performed, it is argued that the Random Forest classifier is performing in a way that the current use of the classifier to recognize the Devanagari Handwritten character and the numerical values with the accuracy rate 87.9% for the considered 123 samples.

A Slice-based Character Recognition Technique for Handwritten Devanagari Script

Abstract: The conventional Character Recognition for English character has been widely analyzed in the last few decades and now highly advanced for creating different technology driven applications. Indian languages which are relatively complicated in terms of geometrical shape are far behind from this perspective. Government office, library, bank and publishing houses now prefer digital document processing to make their services fast and economical. Devanagari is the official script (Hindi language) of India, and spoken by major population of the country. Many researchers have shown interest mainly on recognition of digitally printed Devanagari character. However, very little work has been done in the area of handwritten Devanagari character recognition. Therefore, there is a need to focus in this handwritten Devanagari character recognition as it will help in solving many real life problems like bank check processing, post offices and government offices in India where most of the applications and documents are written in Devanagari characters. In this paper, we have suggested a handwritten Devanagari character recognition method using the topological and geometrical features of the character. First, we cut the characters into slices and then with the help of a slice or a set of slices a feature set is formed to uniquely identify a character. The experimental result shows that the proposed method provides more than 82% accuracy which is quite better than many existing methods.

On-line Handwritten Devanagari Character Recognition using Fuzzy Directional Features.

Abstract: This paper describes a new feature set for use in the recognition of on-line handwritten Devanagari script based on Fuzzy Directional Features. Experiments are conducted for the automatic recognition of isolated handwritten character primitives (sub-character units). Initially we describe the proposed feature set, called the Fuzzy Directional Features (FDF) and then show how these features can be effectively utilized for writer independent character recognition. Experimental results show that FDF set perform well for writer independent data set at stroke level recognition. The main contribution of this paper is the introduction of a novel feature set and establish experimentally its ability in recognition of handwritten Devanagari script.