Character Recognition (Devanagari Script) (original) (raw)

Development of Intelligent Optical Handwritten Character Recognition System for Devanagari Script

2016

1-5Student, Dept. of Computer Science of Engineering, DYPCET, Kolhapur, Maharashtra, India 6Guide, Dept. of Computer Science of Engineering, DYPCET, Kolhapur, Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract Some Indian languages like Hindi, Sanskrit and Marathi are composed from Devanagari script. This makes clear that it is the most widely used script in India. Most ancient documents are written in Devanagari script. The script has 16 vowels and 36 consonants. Unlike, English language Devanagari script does not have any upper or lower case characters. Instead, it has a horizontal line at the top called as shirorekha which joins the characters to form a word. Thus, the report gives a brief review about the development of Optical Handwritten Character Recognition for Devanagari script, the techniques & algorithms which are designed for examining & recognizing the De...

Character Recognition of Offline Handwritten Devanagari Script Using Artificial Neural Network

2016

Document segmentation is one of the important phases in machine recognition of any language. Correct segmentation of individual symbols decides the exactness of character recognition technique. It is used to partitioned image of a string of characters into sub images of individual symbols by segmenting lines and words. Devnagari is the most accepted script in India. It is used for lettering Hindi, Marathi, Sanskrit and Nepali languages. Moreover, Hindi is the third most accepted language in the world. Devnagari documents consist of vowels, consonants and various modifiers. Hence perfect segmentation of Devnagari word is challenging. In this paper a bounded box method for segmentation of documents lines, words and characters and proper recognition of Devanagari characters using variation of Gradient, Structural features and artificial neural network (ANN) is proposed. KeywordsCharacter Segmentation, Character recognition, OCR System, Properties of Devanagari;

A Review on Handwritten Devanagari Character Recognition

2019

Because of the vast variation in writing styles, the handwritten text recognition is takes into account to be challenging task. So, the handwritten character recognition is now an active field of research. In India, a large number of people use Devanagari Script to write their documents, but due to large complexity, research work done on this script is very less compared to English script. Hence, handwritten recognition of Devanagari Script is one of the most demanding research area in the field of pattern recognition. Feature extraction and classification are important steps of OCR which affects the overall accuracy of the character recognition system. This paper gives a detailed review on different techniques used for feature extraction and classification by the researchers over the last few years. Keywords— OCR, Devanagari, Artificial Neural Network, CNN, K-NN, SVM.

A Comparative study on Handwritten Devanagari Character Recognition

2020

Handwritten text recognition is a challenging task because of the vast changes in writing styles. In India, a massive number of people use Devanagari Script to write their documents, but due to large complexity, research work accomplished on this script is much lesser as compared to English script. Hence, recognition of handwritten Devanagari Script is amongst the most demanding research areas in the field of image processing. Feature extraction and recognition are key steps of OCR which affects the accuracy of the character recognition system. This paper gives a comparative study on distinct techniques used for feature extraction and classification by the researchers over the last few years.

Optical Character Recognition for Hindi Language Using Neural-network Approach

Journal of Information Processing Systems, 2013

"Hindi is the most spoken languages in India, with more than 300 million speaking it. As there is no separation between the characters of the text written in Hindi similar to texts written in English, the Optical Character Recognition (OCR) systems developed for Hindi language carries a very poor recognition rate. In this paper we propose an OCR for printed Hindi text in Devanagari script, using Artificial Neural Network (ANN) which improves the efficiency. One of the major reasons for the poor recognition rate is error in character segmentation. Presence of touching characters in the scanned documents further increase the segmentation process thus creating a major problem while designing an effective character segmentation technique. Preprocessing, character segmentation, feature extraction and finally classification & recognitions are the major steps which are followed by a general OCR. The preprocessing tasks considered in the paper are conversion of gray scaled images to binary images, image rectification, and segmentation of document´s textual contents into paragraphs, lines, words and then at the level of basic symbols. The basic symbols, obtained as the fundamental unit from segmentation process are recognized by neural classifier. In this work, three feature extraction techniques: histogram of projection based on mean distance, histogram of projection based on pixel value and vertical zero crossing has been used to improve the rate of recognition. These feature extraction techniques are powerful enough to extract features of even distorted characters/symbols. For developing the neural classifier, back-propagation neural network with two hidden layer is used. The classifier is trained and tested for printed Hindi texts. A performance of approximately 90% correct recognition rate is achieved."

Handwritten character recognition system with Devanagari script (SWARS)

To recognize handwritten Hindi characters automatically is a very difficult because of characters written in different ways like curves and cursively written are differently in various ways. Therefore, these characters are written in distinct sizes, dimension, orientation, format and thickness. Offline written text images from a piece of paper are scanned optically i.e. OCR (optical characters recognition). Devanagari script has 13 vowels and 33 consonants so, an offline handwritten Hindi characters (on SWARS) recognition system using neural network is presented in this paper, Which can be used in popular and common applications like government records, commercial forms, bank cheques, post code recognition, bill processing systems, signature verification and passport readers. In this paper, by using Gradient descent approach, Devanagari script characters are OCR from document images.

Study of Techniques Used For Devanagari Handwritten Character Recognition

With the recent advances in the computing technology, many recognition tasks have become automated. Character Recognition maps a matrix of pixels into characters and words. Recently, artificial neural network theories have shown good capabilities in performing character recognition. In this paper, the application of neural networks in recognizing characters from a handwritten Devanagari script is explored. Asimplified neural approach torecognition of handwrittencharacters is portrayed and discussed.

Devanagari Character Recognition using Image Processing & Machine Learning

IRJET, 2022

In terms of character recognition there are several papers reported and most of them are for English character. This paper focused on Devanagari character recognition from images. Devanagari script is used for many languages such as Sanskrit, Marathi, Nepali and Hindi. Lot of work has been done in character recognition and lot of work is to be done. Devanagari script should be given a special attention so that analysis of this language can be done effectively. This paper presents an approach for recognition of handwritten Devanagari characters, Total Fifty Eighth handwritten characters each having (vowels=220, consonant=2000, digits=2000) resulting in 94640 images are used for this experimentation. The final accuracy is around 90%. The handwritten characters are scanned and on every individual character's image transform is applied so as to get decomposed images of characters. Character recognition provides an alternative way of converting manual text into digital format and reduces the dependence of man power.

Indian script character recognition: a survey

Pattern Recognition, 2004

Intensive research has been done on optical character recognition (OCR) and a large number of articles have been published on this topic during the last few decades. Many commercial OCR systems are now available in the market. But most of these systems work for Roman, Chinese, Japanese and Arabic characters. There are no su cient number of work on Indian language character recognition although there are 12 major scripts in India. In this paper, we present a review of the OCR work done on Indian language scripts. The review is organized into 5 sections. Sections 1 and 2 cover introduction and properties on Indian scripts. In Section 3, we discuss di erent methodologies in OCR development as well as research work done on Indian scripts recognition. In Section 4, we discuss the scope of future work and further steps needed for Indian script OCR development. In Section 5 we conclude the paper.

Survey and Analysis of Devnagari Character Recognition Techniques using Neural Networks

International Journal of Computer Applications, 2012

English Character Recognition techniques have been studied extensively in the last few years and its progress and success rate is quite high. But for regional languages these are still emerging and their success rate is moderate. There are millions of people who speak Hindi and use Devnagari script for writing. As digital documentation in Devnagari script is gaining popularity. Research in Optical Character Recognition (OCR) is very essential especially with an eye on its applications in banks, post offices, defense organizations, library automation, etc. Devnagari Optical Character Recognition needs more attention as it is national language and there is less development in this field due to complexity in the script. This paper describes the current techniques being used for DOCR. The overview of the system is explained with the available techniques and their current status.