Topic
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.
Papers published on a yearly basis
Papers
More filters
TL;DR: In this paper, an artificial neural network based classifier and statistical and structural method based feature extraction method has been employed for the recognition of the Devanagari script, which achieved 98% - 99% accuracy except special characters.
Abstract: Handwriting is the most effective way by which civilized people speaks. Devanagari is the basic Script widely used all over India. Many Indian languages like Hindi, Marathi, Rajasthani are based on Devanagari Script. In the proposed work multistage approach i.e. an artificial neural network based classifier and statistical and structural method based feature extraction method has been employed for the recognition of the script. Optical isolated Marathi words are taken as an input image from the scanner. An input image is preprocessed and segmented. The key step is feature extraction, features are extracted in terms of various structural and statistical features like End points, middle bar, loop, end bar, aspect ratio etc. Feature vector is applied to Self organizing map (SOM) which is one of the classifier of an artificial neural Network.SOM is trained for such 3000 different characters collected from 500 persons. The characters are classified into three different classes. The proposed classifier attains 98% - 99% accuracy except special characters.
5 citations
Patent•
04 Nov 2009TL;DR: In this article, a mechanism for identifying invalid syllables in Devanagari script is described, in which a character type for a character of the text is determined and a new state associated with the character by referencing a state machine with the determined character type and a current state of the script.
Abstract: A mechanism for identifying invalid syllables in Devanagari script is disclosed. A method of embodiments of the invention includes receiving Devanagari text from an application of a computing device for parsing, determining a character type for a character of the Devanagari text, determining a new state associated with the character by referencing a Devanagari state machine with the determined character type and a current state of the Devanagari text, and transmitting an invalid syllable signal to the application for display on a display device to an end user of the application if the determined new state is invalid.
5 citations
01 Jun 2012
TL;DR: A scheme for standardization of stroke order thereby training the user and using the strategy for future isolated Devanagari character recognition in iPhone using Hidden Markov Models (HMM) is proposed.
Abstract: Devanagari is a popular old script used by multiple major languages of the Indian sub-continent such as Hindi, Marathi, Nepali and Sanskrit. The popularity of the script is self-evident from the fact that more than 500 million use it. Complexity associated with unconstrained Devanagari writing is more than English cursive due to the possible variations in the order number, direction and shape of constituent strokes. In this paper, we propose a scheme for standardization of stroke order thereby training the user and using the strategy for future isolated Devanagari character recognition in iPhone. On the basis of manual study of stroke classes, one Hidden Markov Models (HMM) is created for each class which is chosen for character recognition in that HMM class. Standardization of stroke order reduces the number of HMM promoting higher probability and faster recognition of isolated Devanagari character. A few characters among the 20 most occurring characters are taken as samples and their manual study generates certain stroke classes. The second phase deals with the training and recognition of isolated Devanagari character on the basis of the HMM classes. Though there has been some work on Optical Character Recognition (OCR) of Devanagari character, recognition on a smartphone platform like iPhone is a new and promising field.
5 citations
08 Apr 2021
TL;DR: In this article, a character recognition system for Devanagari script using various machine learning classifiers like Decision Tree classifier, Nearest Centroid classifier and K Nearest Neighbors classifier was proposed.
Abstract: It is a very difficult task to manually process the handwritten documents due to varieties of handwritten scripts and lack of associated language dictionary to interpret documents. Most of the large companies as well as small-scale industries want to automate the process of script recognition. The big challenge is to make machines recognize the hand-printed scripts. Humans can recognize handwritten or hand-printed words after gaining knowledge of a specific language. In the same way, machines should be trained to recognize the handwritten scripts. This process of transferring human knowledge to computers should be automated. The proposed research work attempts to automate the character recognition system for Devanagari script using various machine learning classifiers like Decision Tree classifier, Nearest Centroid classifier, K Nearest Neighbors classifier, Extra Trees classifiers and Random Forest classifier. The performance of all the classifiers is evaluated using accuracy parameter as success criteria. The Extra Trees classifiers and Random Forest classifier is proved to better than other classifiers with 78% and 77% of accuracy respectively. The robustness to picture quality, writing style, font size is the novelty of the OCR system which makes it ideal to use.
5 citations