Combining multiple thresholding binarization values to improve OCR output

Lund, William B.; Kennard, Douglas J.; Ringger, Eric K.

doi:10.1117/12.2006228

Cited by 37 publications

(32 citation statements)

References 25 publications

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“…Although the adaptive thresholding methods are more suitable for images with low quality, none of them is suitable in all cases [17,27]. The performance of an approach differs from an image to another and so the resulting binarised image.…”

Section: Lcatp Descriptormentioning

confidence: 99%

“…The performance of an approach differs from an image to another and so the resulting binarised image. Therefore combining various methods may improve the performance of the final results [17,27]. Our approach incorporates the LTP with three different methods of local adaptive thresholding: modified Niblack (T N ) [18], Wolf (T W ) [19] and Yung (T Y ) [20] to create the LCATP descriptor.…”

Section: Lcatp Descriptormentioning

confidence: 99%

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Texture descriptor based on local combination adaptive ternary pattern

Sandid

Douik

2015

IET Image Processing

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Material recognition has several applications, such as image retrieval, object recognition and robotic manipulation. To make the material classification more suitable for real-world applications, it is fundamental to satisfy two characteristics: robustness to scale and to pose variations. In this study, the authors propose a novel discriminant descriptor for texture classification based on a new operator called local combination adaptive ternary pattern (LCATP) descriptor used to encode both colour and local information. They start by building the LCATP descriptor using a combination of three different adaptive thresholding techniques. Moreover, they present a novel operator, mean histogram (MH), used jointly with the LCATP in order to incorporate colour information into the descriptor. This approach is then extended to four different colour spaces: LC 1 C 2 , I 1 I 2 I 3 , LSH uv and O 1 O 2 O 3 . The final descriptor, LCATP fusion (LCATP_F), is produced by fusing the basic histogram (H) and MH extracted from the different colour spaces. Finally, the LCATP_F descriptor properties, such as the robustness to scale and pose changes are evaluated using the challenging KTH-textures under varying illumination, pose and scale (TIPS2b) dataset along with the least squares support vector machines classifier. The obtained experimental results, using the LCATP_F descriptor, show a significant improvement with respect to the state-of-the-art results.

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Section: Lcatp Descriptormentioning

confidence: 99%

Section: Lcatp Descriptormentioning

confidence: 99%

Texture descriptor based on local combination adaptive ternary pattern

Sandid

Douik

2015

IET Image Processing

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“…One direction of work ensembles outputs from multiple OCR engines for the same input and selects the best word recognition as the final output (Klein et al, 2002;Cecotti and Belayd, 2005;Lund and Ringger, 2009;Lund et al, 2011;Lund et al, 2013a;Lund et al, 2013b). Klein et al (2002) show that combining complementary result from different OCR models leads to a better output.…”

Section: Related Workmentioning

confidence: 99%

Statistical learning for OCR error correction

Mei

Islam

Moh’d

et al. 2018

Information Processing & Management

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“…[5][6][7][8][9] One of the first efforts to improve the performance of OCR systems is preprocessing an input scene using binarization, segmentation, skew detection, dewarping and image improving algorithms. [10][11][12][13][14][15] However, in some cases this is not enough because they do not work with noisy or distorted images.…”

Section: Introductionmentioning

confidence: 95%

Nonlinear filtering for character recognition in low quality document images

Diaz-Escobar

Kober

2014

SPIE Proceedings

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Optical character recognition in scanned printed documents is a well-studied task, where the captured conditions like sheet position, illumination, contrast and resolution are controlled. Nowadays, it is more practical to use mobile devices for document capture than a scanner. So as a consequence, the quality of document images is often poor owing to presence of geometric distortions, nonhomogeneous illumination, low resolution, etc. In this work we propose to use multiple adaptive nonlinear composite filters for detection and classification of characters. Computer simulation results obtained with the proposed system are presented and discussed.

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Combining multiple thresholding binarization values to improve OCR output

Cited by 37 publications

References 25 publications

Texture descriptor based on local combination adaptive ternary pattern

Texture descriptor based on local combination adaptive ternary pattern

Statistical learning for OCR error correction

Nonlinear filtering for character recognition in low quality document images

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