Comparative Study of Devnagari Handwritten Character Recognition Using Different Feature and Classifiers

Pal, Umapada; Wakabayashi, Tetsushi; Kimura, Fumitaka

doi:10.1109/icdar.2009.244

Cited by 92 publications

(49 citation statements)

References 10 publications

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“…Nepali, Sanskrit, and Marathi are also written in devanagari script moreover Hindi is the national language of India. Therefore, the work on Devanagari script is very useful for the country [4].The alphabet of modern Hindi consists of 13 vowels and 34 consonants. The writing style in Devanagari script is from left to right.…”

Section: Properties Of Devanagari Scriptmentioning

confidence: 99%

“…A consonant or vowel sometimes takes a compound orthographic shape, which we call as compound characters. Compound characters can be combinations of a consonant and a vowel [4]. In that article, we considered 47 basic characters from a self-organized dataset which are shown in figures.…”

Section: Properties Of Devanagari Scriptmentioning

confidence: 99%

“…In that article, we considered 47 basic characters from a self-organized dataset which are shown in figures. he complexity of a handwritten character recognition system increases mostly due to different writing styles [4]. Most of the errors in Hindi OCR's arise due to confusion among the similar shaped characters [11].…”

Section: Properties Of Devanagari Scriptmentioning

confidence: 99%

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Unconstrained Character Classification For Off-Line Handwritten Devanagari Script Using Multi-Class Model For SVM

Kumar¹

2017

IJRASET

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Hindi script is being used in various languages, for instance Marathi, Rajasthan, Sanskrit, and Nepali and it is also the script of Devanagari. It is observed that errors in classification mainly due to complex structures, incorrect segmentation and high unevenness in writing styles, classification of characters from the unconstrained script has become a burning vicinity of interest for researchers. Computer-based pattern recognition is a process that involves preprocessing, feature extraction, feature selection, and classification. In that article, we have extracted features from HOG, the novelty of this approach to attain better accuracy and reduce misclassification as well as for classification of handwritten characters with a multiclass model for SVM. Implementation has been performed using a self-created dataset of 40 users for handwritten Hindi characters. The experimental results obtained from this self-created dataset described the effectiveness of this system. The proposed system has faster speed and higher accuracy than the traditional Hindi OCR's. Enormous applications and the future necessities of optical character recognition area open new paths for researchers. An effort is made to address the most crucial consequences and it is also tried to foreground the better directions of the research till date. Our experimental results present the high performance of these features when classified using SVM classification.

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Section: Properties Of Devanagari Scriptmentioning

confidence: 99%

Section: Properties Of Devanagari Scriptmentioning

confidence: 99%

See 1 more Smart Citation

Unconstrained Character Classification For Off-Line Handwritten Devanagari Script Using Multi-Class Model For SVM

Kumar¹

2017

IJRASET

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“…These are the reason that made researchers to find techniques that will improve the knack of computers to characterize and recognize handwritten characters [3,4]. OCR is not only a new developing area due to many potential applications such as bank check processing, postal mail sorting, automatic reading of tax forms, and reading various handwritten and printed text and non-text documents [5,6]. It is also a benchmark for testing and verifying new pattern recognition theories and algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Structured Analytical Approach to Handwritten Marathi vowels Recognition

Patil¹,

Adhiya²,

Ramteke³

2011

IJCA

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ABSTRACT:In OCR domain, it is now widely accepted that a single feature extraction method and single classification algorithm can't yields better performance rate. It is therefore, a compound feature extraction approach based on structural analysis for recognition of offline handwritten Marathi vowels is proposed. Though, Moment invariant technique is well experimented by many researchers, an attempt is made to enhance the existing results by extracting various supportive features like affine invariant moments, image thinning, structuring the image in box format, etc. These features are independent of size, slant, orientation, translation and other variations in handwritten characters. 5 samples of each vowel from 25 different people have been sampled and database was prepared. After segmentation, an individual image is resized to 50X50. 33 different features were evaluated for each character. The Fuzzy Gaussian Membership Function has been adopted for classification. The main objective of the paper is to test the possibility of using the MI, AMI combination of both for recognition of Handwritten Marathi vowels. The results show the satisfactory performance rate.

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“…4 present examples of characters and matras from the real world image data set. [13,14] created for the OCR of the scanned documents. These samples are collected from the designed user fill-in sheets.…”

Section: Description Of the Devanagari Scriptmentioning

confidence: 99%

Devanagari Character Recognition in the Wild

Ramanamurthy¹,

Roy²,

Narang³

et al. 2012

IJCA

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This papers examines the issues in recognizing the Devanagari characters in the wild like sign boards, advertisements, logos, shop names, notices, address posts etc. While some works deal with the issues in recognizing the machine printed and the handwritten Devanagari characters, it is not clear if such techniques can be directly applied to the Devanagari characters captured in the wild. Moreover in the recent times a lot of research has been conducted in the field of object categorization and localization. It would be interesting to investigate if the state-of-the-art tools for object categorization can also be applied to the recognition of the Devanagari characters. The idea is to view the isolated characters as objects so as to detect them in the wild. The ability to recognize the Devanagari characters in the wild will be very useful in the Internet services like Google street view and its associated applications. So, a detailed study of the Devanagari character recognition using the state-of-the-art character recognition and object recognition tools has been carried out to compute the best performance. This serve as a baseline for the comparison for the future works.There is no benchmark database to conduct studies on the Devanagari character recognition in the wild. So a database of 40 Devanagari character categories has been created from 200 pictures of the images in the wild. GENERAL TERMS:Character recognition, Devanagari script, local and global feature selection, object recognition.

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Comparative Study of Devnagari Handwritten Character Recognition Using Different Feature and Classifiers

Cited by 92 publications

References 10 publications

Unconstrained Character Classification For Off-Line Handwritten Devanagari Script Using Multi-Class Model For SVM

Unconstrained Character Classification For Off-Line Handwritten Devanagari Script Using Multi-Class Model For SVM

A Structured Analytical Approach to Handwritten Marathi vowels Recognition

Devanagari Character Recognition in the Wild

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