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.

Marathi character recognition using ant miner algorithm

Abstract: Optical Character Recognition (OCR) is an interesting and challenging field of research in pattern recognition, artificial intelligence and machine vision and is used in many real life applications. The work done for the recognition of Devanagari handwritten script is negligible in literature despite it is being used by millions people in India and abroad and it has numerous applications. Research on Optical Character Recognition OCR of Devnagari script is very challenging due to the complex structural properties of the script that are not observed in most other scripts. Devnagari is the script for Marathi. The Marathi language contains 49 distinct characters, 12 vowels and 37 consonants. Recognition of Devnagari characters poses great challenge due to the large variety of symbols and their proximity in appearance. Feature extraction and classification are the two very important steps in Optical character recognition. In this paper we have used three sets of feature extraction techniques namely Hu's moment invariant, zoning with Hu moment and Zoning with Hu's moment and radon transform. Here we have proposed Ant Miner Algorithm (AMA) for classification. The AMA is a rule-based approach. The rules are incrementally tuned during the training. The result of this experiment is a 96.94% recognition rate of the training set and 82.21% recognition rate of unseen data test

Study on Automated Approach to Recognize Characters for Handwritten and Historical Document

Abstract: Script recognition is the mechanism of automatic script analysis and recognition whereby intensive study has been carried out and a significant amount of papers on this problem have been released over the past. But there are still a few issues to be solved, particularly in Indian historical manuscripts. This literature examines the Script recognition with reference to multi-script document and different historical scripts such as Kurdish-Latin, Devanagari, Grantha, Arabic handwritten characters, Bangladesh, Devanagari and Gurumukhi, ancient Chinese, Arabic, Nam Character, Greek, Nastalique Urdu, Georgian handwritten, Nandinagari, and Hebrew, which provide the course of study that focuses on the framework for script recognition. This review concentrates on scope of prediction, dataset type, the methods used for data preprocessing, and measures of performance used for analysis. On the basis of this survey, Current research constraints have been recognized and future study specifications are emphasized in the area of modeling historical manuscripts. CCS Concepts:

Marathi - English Named Entities Forward MachineTransliteration Using Linguistic and Metrical Approach

Abstract: Machine transliteration is required in machine translation to transliterate words which are not present in dictionary such as names of persons, locations, cities and villages, names of roads and building etc It is noticed that the machine transliteration is less studied for the language pair Marathi-English Most of the present research in this area is done by using linguistic rule based and grapheme/phoneme based statistical approaches with the help of machine learning tools This paper proposes Marathi to English forward machine transliteration of named entities of Indian-origin using hybrid approach which is the combination of linguistic rules and metrical approach Proposed rule-based transliteration method uses phoneme model of the machine transliteration which uses the phonetic mapping between the source language Marathi written using Devanagari script and target language English written using Roman script The key concept of this method is schwa deletion based on the stress of syllable after phonetic mapping This approach requires full consonant based input and uses syllable based metrical approach for schwa deletion Metrical approach works on stress of syllable whether it is unstressed or stressed in the given named entity The experimental result showed that the absolute performance of the proposed method is high as compare to existing grapheme based approaches