A hierarchical approach to recognition of handwritten Bangla characters

Basu, Subhadip; Das, Nibaran; Sarkar, Ram; Kundu, Mahantapas; Nasipuri, Mita; Basu, Dipak Kumar

doi:10.1016/j.patcog.2009.01.008

Cited by 103 publications

(49 citation statements)

References 17 publications

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“…Basu et al [11] presented recognition system for postal address code for Latin, Devanagari, Bangla and Urdu. Hough transform is used for localization and isolation.…”

Section: Urdu Digit Recognitionmentioning

confidence: 99%

Arabic Script based Digit Recognition Systems

Naz¹,

Ahmed²,

Ahmad³

et al. 2016

Proceedings of the 2015 International Conference on Recent Advances in Computer Systems

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“…Basu et al [11] presented recognition system for postal address code for Latin, Devanagari, Bangla and Urdu. Hough transform is used for localization and isolation.…”

Section: Urdu Digit Recognitionmentioning

confidence: 99%

Arabic Script based Digit Recognition Systems

Naz¹,

Ahmed²,

Ahmad³

et al. 2016

Proceedings of the 2015 International Conference on Recent Advances in Computer Systems

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“…Garain et al [7] have achieved a recognition accuracy of 96.3 % for Bangla handwritten character recognition. Swethalakshmi et al [21] have proposed a handwritten Devanagri and Telugu character recognition system using SVM whereas Basu et al [2] have presented a hierarchical approach for handwritten Bangla characters recognition. They have achieved a recognition accuracy of 72.06 % with MLP classifier.…”

Section: Related Workmentioning

confidence: 99%

A Novel Hierarchical Technique for Offline Handwritten Gurmukhi Character Recognition

Kumar

Jindal

Sharma

2014

Natl. Acad. Sci. Lett.

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The increasing need of a handwritten character recognition system in the Indian offices such as banks, post offices and so forth, has made it an imperative field of research. In present paper, Authors have presented a novel hierarchical technique for isolated offline handwritten Gurmukhi character recognition. A robust feature set of 105 feature elements is proposed under this work for recognition of offline handwritten Gurmukhi characters using four types of topological features, namely, horizontally peak extent features, vertically peak extent features, diagonal features, and centroid features. For classification Support Vector Machines (SVMs) classifier has been used in this work. SVMs classifier has been considered with four different kernels, namely, linear kernel, polynomial kernel, RBF kernel and sigmoid kernel. For training and testing of a classifier, we have used 3,500 samples of isolated offline handwritten Gurmukhi characters written by one hundred different writers. Maximum recognition accuracy of 91.80 % have been achieved with proposed technique, while using PCA feature set and SVM with a linear kernel classifier.

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“…Optical character recognition (OCR) is an active field, particularly for handwritten documents in such languages as Roman [45], Arabic [46], Chinese [47], and Indian [48]. Several Chinese character recognition methods have been proposed, with the best known being the transformation invariant matching algorithm [49], adaptive confidence transform based classifier combination [50], probabilistic neural networks [51], radical decomposition [52], statistical character structure modeling [53], Markov random fields [54], and affine sparse matrix factorization [55].…”

Section: Musical Symbol Recognitionmentioning

confidence: 99%

An optical music recognition system for traditional Chinese Kunqu Opera scores written in Gong-Che Notation

Chen

Sheu

2014

J AUDIO SPEECH MUSIC PROC.

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This paper presents an optical music recognition (OMR) system to process the handwritten musical scores of Kunqu Opera written in Gong-Che Notation (GCN). First, it introduces the background of Kunqu Opera and GCN. Kunqu Opera is one of the oldest forms of musical activity, spanning the sixteenth to eighteenth centuries, and GCN has been the most popular notation for recording musical works in China since the seventh century. Many Kunqu Operas that use GCN are available as original manuscripts or photocopies, and transforming these versions into a machine-readable format is a pressing need. The OMR system comprises six stages: image pre-processing, segmentation, feature extraction, symbol recognition, musical semantics, and musical instrument digital interface (MIDI) representation. This paper focuses on the symbol recognition stage and obtains the musical information with Bayesian, genetic algorithm, and K-nearest neighbor classifiers. The experimental results indicate that symbol recognition for Kunqu Opera's handwritten musical scores is effective. This work will help to preserve and popularize Chinese cultural heritage and to store Kunqu Opera scores in a machine-readable format, thereby ensuring the possibility of spreading and performing original Kunqu Opera musical scores.

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A hierarchical approach to recognition of handwritten Bangla characters

Cited by 103 publications

References 17 publications

Arabic Script based Digit Recognition Systems

Arabic Script based Digit Recognition Systems

A Novel Hierarchical Technique for Offline Handwritten Gurmukhi Character Recognition

An optical music recognition system for traditional Chinese Kunqu Opera scores written in Gong-Che Notation

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