IJERT-Handwritten Devanagari Character Recognition (original) (raw)
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Handwritten Devanagari Character Recognition
2014
Recognition of Devanagari character consists of Image correction, segmentation and character recognition. Image correction digitizes the input characters making it available for further processing. Principle component analysis is used to discover the hidden and unclear part and segmentation separates individual characters to identify each character. The most crucial part of any character recognition system is the process of segmentation as characters are recognized individually. The result of recognition is dependent on the accuracy of segmentation. For extraction and recognition we used Eigen space method which uses Gerschgorin's theorem for comparison. Handwritten Devanagari script is nowadays a popular topic for researchers as less work is done on this topic. Handwritten Devanagari characters are difficult to recognize due to the presence of header line and various modifiers. Recognition of fused characters is also a major concern for researchers as fused character is treated as a single character resulting in an error.
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.
Character Recognition (Devanagari Script)
2015
Character Recognition is has found major interest in field of research and practical application to analyze and study characters in different languages using image as their input. In this paper the user writes the Devanagari character using mouse as a plotter and then the corresponding character is saved in the form of image. This image is processed using Optical Character Recognition in which location, segmentation, pre-processing of image is done. Later Neural Networks is used to identify all the characters by the further process of OCR i.e. by using feature extraction and post-processing of image. This entire process is done using MATLAB.
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.
A Slice-based Character Recognition Technique for Handwritten Devanagari Script
ICSES Transaction on Image Processing and Pattern Recognition, 2020
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.
International Journal of Engineering Research and Technology (IJERT), 2014
https://www.ijert.org/recognition-of-handwritten-devnagari-characters-through-segmentation-and-artificial-neural-networks https://www.ijert.org/research/recognition-of-handwritten-devnagari-characters-through-segmentation-and-artificial-neural-networks-IJERTV1IS6065.pdf Handwritten character recognition is the ability of a computer to receive and interpret intelligible handwritten input from sources such as paper documents, photographs, touch-screens and other devices.Handwritten Marathi Characters are more complex for recognition than corresponding English characters due to many possible variations in order, number, direction and shape of the constituent strokes. The main purpose of this paper is to introduce a new method for recognition of offline handwritten devnagari characters using segmentation and Artificial neural networks. The whole process of recognition includes two phases-segmentation of characters into line, word and characters and then recognition through feed-forward neural network.
Comparative Study of Segmentation and Recognition Methods for Handwritten Devnagari Script
International Journal of Computer Applications, 2014
Script recognition systems for various languages have gain importance in recent decades and are the area of deep interest for many researchers. English Character Recognition (CR) has been extensively studied in the last half century and progressed to a level, sufficient to produce technology driven applications. But same is not the case for Indian languages which are complicated in terms of structure and computations. Indian scripts present great challenges to an OCR designer due to the large number of letters in the alphabet, the sophisticated ways in which they combine, and the complicated graphemes they result in. Devnagari(Hindi) being the national language of India, spoken by more than 500 million people, should be given special attention so that document retrieval and analysis of rich ancient and modern Indian literature can be effectively done. This article is intended to serve as a guide and update for the readers, working in the Handwritten Devanagari Script Recognition (HDSR) area. An overview of HDSR systems is presented and the available HDSR techniques are reviewed. The current status of HDSR is discussed and directions for future researches are suggested.
Devanagari Character Recognition: A Short Review
International Journal of Computer Applications, 2012
Optical character recognition is a vital task in the field of pattern recognition. English character recognition has been extensively studied by many researchers but in case of Indian languages which are complicated; the research work is very limited. Devanagari is an indian script used by huge number of indian people. Devanagari forms the basis for several indian languages including Hindi, Sanskrit, Kashmiri, Marathi and so on. This article presents a review of earlier research work related to devanagari character recognition along with some applications of optical character recognition system.