A Hybrid Scheme for Recognition of Handwritten Bangla Basic Characters Based on HMM and MLP Classifiers

Bhattacharya, Ujjwal; Parui, Susanta Kumar; Shaw, Bikash

doi:10.1142/9789812772381_0015

Cited by 9 publications

(10 citation statements)

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“…HMM, models can be used for end-to-end recognition [189] and online recognition [190]. Other tasks of DAR where the use of HMM has been proposed are Character classification, character recognition [183], word recognition [151], [159], [181], [191], [192], line recognition [188], numeral recognition [193] and text recognition [179].…”

Section: D) Classifier Ensemble Methodsmentioning

confidence: 99%

Document Analysis and Recognition: A survey

NIGAM¹,

Verma²,

Nagabhushan³

2023

Preprint

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<p>The journey of research for Document Analysis and Recognition (DAR) started with the problem of automatic character recognition. Today, it has covered a vast span of recognition tasks such as text recognition, script identification, writer identification, word spotting, signature verification etc., in scripts like Roman, Arabic, Chinese, Japanese, Brahmi etc. Extensive advancements in deep learning techniques have achieved state-of-the-art results for various DAR tasks. This work explores the challenges from different perspectives and reviews the techniques for online/offline and printed/handwritten DAR tasks. We examine the research works with the view of script-related challenges. Various datasets for DAR are categorized into historic, printed and handwritten datasets. We present a comprehensive survey of challenges, techniques, datasets, evaluation metrics, script-related perspectives and potential future directions for DAR.</p>

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Section: D) Classifier Ensemble Methodsmentioning

confidence: 99%

Document Analysis and Recognition: A survey

NIGAM¹,

Verma²,

Nagabhushan³

2023

Preprint

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“…Pal and Chaudhuri [5] have written an interesting survey that deals mainly with the OCR research of printed Indian scripts. Some work on isolated Bangla character recognition has been reported in recent past [1]. Attempt of reading city names in Bangla from postal documents is also reported [6].…”

Section: Related Workmentioning

confidence: 99%

Perceptive Vision for Headline Localisation in Bangla Handwritten Text Recognition

Lemaitre

Chaudhuri

Coiiasnon

2007

Ninth International Conference on Document Analysis and Recognition (ICDAR 2007) Vol 2

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International audienceIn this paper, we propose to give tools for Bangla handwriting recognition. We present a mechanism to segment documents into text lines and words, and more specifically to detect headline position in each word. Indeed, this headline is an horizontal line on the upper part of most of characters, which is characteristic of Bangla writing. Its localisation is a new approach that can improve text recognition quality. This headline is detected into words inside text lines thanks to a notion of perceptive vision: at a certain distance, text lines appear as line-segments that give the global orientation of words. Watching closer may help to give the exact position of the headline. Consequently, this work is mainly based on applying a segment extractor at different image resolutions and combining extracted information in order to compute the headlines. Our line-segment extractor is based on Kalman filtering

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“…R J Ramteke et al [6] have proposed a method based on invariant moments and the divisions of image for the recognition of numerals and achieved 92% accuracy. U. Bhattacharya et al [7] have used a combination of ANN (Artificial Neural Net-work) and HMM (Hidden Markov Model) classifier on 16273 samples of Handwritten Devanagari Numerals and obtained 95.64% accuracy. N Sharma et al [18] have proposed a quadratic qualifier based technique and used 22546 samples for his experiment and achieved 98.86% accuracy.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Devanagari Handwritten Numerals using Gradient Features and SVM

Aggarwal¹,

Rani²,

Dhir³

2012

IJCA

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Recognition of Indian languages is a challenging problem. In Optical Character Recognition (OCR), acharacter or symbol to be recognized can be machine printed or handwritten characters/numerals. Several approaches in the past have been proposed that deal with problem of recognition of numerals/character depending on the type of feature extracted and way of extracting them. In this paper also a recognition system for isolated Handwritten Devanagari Numerals has been proposed. The proposed system is based on the division of sample image into sub-blocks and then in each sub-block Strength of Gradient is accumulated in 8 standard directions in which Gradient Direction is decomposed resulting in a feature vector with dimensionality of 200. Support Vector Machine (SVM) is used for classification. Accuracy of 99.60% has been obtained by using standard dataset provided by ISI (Indian Statistical Institute) Kolkata.

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A Hybrid Scheme for Recognition of Handwritten Bangla Basic Characters Based on HMM and MLP Classifiers

Cited by 9 publications

References 0 publications

Document Analysis and Recognition: A survey

Document Analysis and Recognition: A survey

Perceptive Vision for Headline Localisation in Bangla Handwritten Text Recognition

Recognition of Devanagari Handwritten Numerals using Gradient Features and SVM

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