Handwritten Digit Recognition by Combining SVM Classifiers

Gorgevik, D.; Cakmakov, Dusan

doi:10.1109/eurcon.2005.1630221

Cited by 30 publications

(15 citation statements)

References 9 publications

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“…To achieve this step a Support Vector Machine has been used. While most of SVM techniques are based on a single SVM, we used a set of SVM specialized on extracting specific features, exploiting the trend of independent classifiers to recognize the same true positives but different false positives [8]. The approach is shown in Fig.…”

Section: Proposed Methodsmentioning

confidence: 99%

Text localization from photos

Guarnera

Messina

Ardizzone

et al. 2009

2009 Digest of Technical Papers International Conference on Consumer Electronics

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In this paper a new text extraction algorithm is proposed. In real scenes the text is usually overlapped or is part of the background. To identify the text regions, in complex conditions, a method exploiting a "multi-resolution feature based method" for extracting text with undefined dimension has been developed. Once identified, the multi-resolution information are merged and skimmed through a set of Support Vector Machines (SVM). The tests and the comparisons with other techniques, performed on heterogeneous images, have shown the effectiveness of the proposed.

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Section: Proposed Methodsmentioning

confidence: 99%

Text localization from photos

Guarnera

Messina

Ardizzone

et al. 2009

2009 Digest of Technical Papers International Conference on Consumer Electronics

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“…Gorgevik et al [1] developed support vector machines (SVM) based handwritten digits recognition system. They extracted four types of features from each digit image 1) projection histograms, 2) contour profiles, 3) ring-zones and 4) Kirsch features.…”

Section: Related Researchmentioning

confidence: 99%

Handwritten Digit Recognition Using DCT and HMMs

Ali¹,

Ghani²

2014

2014 12th International Conference on Frontiers of Information Technology

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Handwritten digits recognition has been an interesting area due to its applications in several fields. Recognition of bank account numbers and zip codes are a few examples. Handwritten digits recognition is not a trivial task due to presence of large variation in writing style in available data. In order to cope with this problem both features and classifier need to be efficient. In this research, transformation based features, Discrete Cosine Transform (2D-DCT), have been used. Hidden Markov models (HMMs) have been applied as classifier. The proposed algorithm has been trained and tested on Mixed National Institute of Standards and Technology (MNIST) handwritten digits database. The algorithm provides promising recognition results on MNIST database of handwritten digits.12th International Conference on Frontiers of Information Technology 978-1-4799-7505-1/14 $31.00

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“…In [1], the slant angle is estimated by the inclination of the line connecting the gravity centers of the top 25% part and the bottom 25% part of the image. Then a sub-pixel shear transformation is performed in order to remove the estimated inclination.…”

Section: Literature Surveymentioning

confidence: 99%

“…The skew-detection was implemented based upon the algorithm proposed by [1] in which the skew angle was estimated as the inclination of the line connecting the gravity centers of the top 25% part and the bottom 25% part of the acquired textual image. The textual image was first scanned vertically for obtaining the upper 25% and the lower 25%.…”

Section: The Proposed Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

A novel approach to skew-detection and correction of English alphabets for OCR

Chinara

Nath

Mishra

et al. 2012

2012 IEEE Student Conference on Research and Development (SCOReD)

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Abstract-Optical Character Recognition has been a challenging field in the advent of digital computers. It is needed where information is to be readable both to humans and machines. The process of OCR is composed of a set of pre and post processing steps that decide the level of accuracy of recognition. This paper deals with one of the pre-processing steps involved in the OCR process i.e. Skew (Slant) Detection and Correction. The proposed algorithm implemented for skew-detection is termed as the COG (Centre of Gravity) method and for that of skew-correction is Sub-Pixel Shifting method. The algorithm has been kept simple and optimized for efficient skew-detection and correction. The performance analysis of the algorithm after testing has been aptly demonstrated.

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Handwritten Digit Recognition by Combining SVM Classifiers

Cited by 30 publications

References 9 publications

Text localization from photos

Text localization from photos

Handwritten Digit Recognition Using DCT and HMMs

A novel approach to skew-detection and correction of English alphabets for OCR

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