A Novel Bilingual OCR for Printed Malayalam-English Text Based on Gabor Features and Dominant Singular Values

Philip, Bindu; Samuel, R. D. Sudhaker

doi:10.1109/icdip.2009.50

Cited by 14 publications

(5 citation statements)

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“…Bindu Philip and R. D. Sudhaker Samuel [11] addressed the problem of bilingual character recognition using Gabor features for script identification and Dominant Singular Values as features for classification. The proposed algorithm has been tested successfully and an overall recognition rate of 96.5% is achieved.…”

Section: IImentioning

confidence: 99%

Performance assessment of different image sizes for printed Gujarati and English digits using template matching

Chaudhari

Gulati

2014

2014 International Conference on Contemporary Computing and Informatics (IC3I)

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This paper presents a system for separation and recognition of offline printed Gujarati and English digits using template matching. Sample images of different quality of papers were collected. They were scanned at 200 dpi. Various preprocessing operations were performed on the digitized images followed by segmentation. Segmented image of various sizes was normalized to get an image of uniform size. Then the pixel density was calculated as binary pattern and a feature vector was created. These features were used in template matching for the classification of digits. The recognition rate was tested on images of 3 different sizes viz. 24 x 24, 32 x 40, and 48 x 48 for offline printed Gujarati and English digits. We collected 200 image samples which include more than 4200 symbols of both Gujarati and English digits. The results were evaluated for different image sizes of 24 x 24, 32 x 40, and 48 x 48. The overall recognition rates were 97.43, 98.30, and 97.28 for Gujarati digits and 99.07, 98.88, and 99.34 for English digits respectively.

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Section: IImentioning

confidence: 99%

Performance assessment of different image sizes for printed Gujarati and English digits using template matching

Chaudhari

Gulati

2014

2014 International Conference on Contemporary Computing and Informatics (IC3I)

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“…They performed CC analysis and did not distort any character to avoid restoration stage. Philip and Samuel (2009) proposed a bilingual OCR based upon Gabor features and dominant singular values and achieved 96.5% recognition accuracy with two stage NN classification for printed Malayalam and English documents. Khoddami and Behrad (2010) identified bilingual scripts from multi-font printed documents through KNN and used rotation and scale invariant curvature scale-space features.…”

Section: Study Ii: a Review Of Bilingual Processing Systems With Othe...mentioning

confidence: 99%

“…Chaudhuri and Pal (1997a), Pal and Tripathy (2009), Bag andHarit (2013), andTripathy et al (2016) proposed OCR with DC3 documents, Naveena et al (2012) proposed it with DC4 documents, and Henge and Rama (2016) and Phangtriastu et al (2017) presented it with DC3 and DC4 documents. DasBebartta and Mohanty (2017), Jawahar et al (2003), Kunte and Samuel (2007), and Ray et al (2015) proposed bilingual OCR with DC6 documents, whereas Philip and Samuel (2009) presented bilingual OCR with DC3 documents. Garain and Chaudhuri (2001) provided a character segmentation method, and Chaudhuri and Pal (1997b) and detected skew and multi-skew, respectively, with DC3 documents only.…”

Section: Study II Findings For Applications and Dcsmentioning

confidence: 99%

Advanced Applications on Bilingual Document Analysis and Processing Systems

Puri

Singh

2020

International Journal of Applied Metaheuristic Computing

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Today, rapid digitization requires efficient bilingual non-image and image document classification systems. Although many bilingual NLP and image-based systems provide solutions for real-world problems, they primarily focus on text extraction, identification, and recognition tasks with limited document types. This article discusses a journey of these systems and provides an overview of their methods, feature extraction techniques, document sets, classifiers, and accuracy for English-Hindi and other language pairs. The gaps found lead toward the idea of a generic and integrated bilingual English-Hindi document classification system, which classifies heterogeneous documents using a dual class feeder and two character corpora. Its non-image and image modules include pre- and post-processing stages and pre-and post-segmentation stages to classify documents into predefined classes. This article discusses many real-life applications on societal and commercial issues. The analytical results show important findings of existing and proposed systems.

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“…Hence extensive work is happening in this field on different scripts [12]. But on Indian language scripts, very little work is reported and only a few research papers are available [1] [6][10] [11]. A study of the literature reveals that the difficulty of the problem has two aspects.…”

Section: Introductionmentioning

confidence: 99%

“…But these are more sensitive to noise in the neighborhood [8]. Some use wavelet transformation [6][9], Gabor transformation [2] [11] etc. Gabor Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.…”

Section: Introductionmentioning

confidence: 99%

Using moments features from Gabor directional images for Kannada handwriting character recognition

Ragha

Sasikumar

2010

Proceedings of the International Conference and Workshop on Emerging Trends in Technology

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Handwriting character recognition (HCR) for Indian Languages is an important problem where there is relatively little work has been done. In this paper, we investigate the moments features on Kannada handwritten basic character set of 49 letters. Moments features are extracted from the preprocessed original images by most of the researchers. Kannada characters are curved in nature with some symmetry observed in the shape. This information can be best extracted as a feature if we extract moment features from the directional images. So we are finding 4 directional images using Gabor wavelets from the dynamically preprocessed original images. We then extract moments features from them. The comparison of moments features of 4 directional images with original images when tested on Multi Layer Perceptron with Back Propagation Neural Network shows an average improvement of 13% from 72% to 85%. The mean performance of the system with these two features together is 92%.

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A Novel Bilingual OCR for Printed Malayalam-English Text Based on Gabor Features and Dominant Singular Values

Cited by 14 publications

References 5 publications

Performance assessment of different image sizes for printed Gujarati and English digits using template matching

Performance assessment of different image sizes for printed Gujarati and English digits using template matching

Advanced Applications on Bilingual Document Analysis and Processing Systems

Using moments features from Gabor directional images for Kannada handwriting character recognition

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