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

Chinara, Chinmay; Nath, Nishant; Mishra, Subhajeet; Sahoo, Soubhagya Kumar; Ali, Farida

doi:10.1109/scored.2012.6518646

Cited by 2 publications

(6 citation statements)

References 7 publications

(7 reference statements)

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“…which were skewed in the entire range of 0 0 to360 0 . Cinara et al [34] used the centre of gravity [43] for detecting skew angle in the document image. W. Postl [168] used Fast Fourier Transform for finding the skew angle.…”

Section: Other Methodsmentioning

confidence: 99%

An Overview of Existing Literature on Document Skew Detection

(Corresponding Author),

BHATTACHARYA,

CHAUDHURI

2023

MJCS

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This article presents somewhat detail discussions on document image skew detection methods available in the existing literature. We first categorise the document images into several types and then we discuss different methods available to detect the skew angle of document images belonging to the individual categories. Various methods of document image skew detection include Hough Transform, Principal Components Analysis, Projection Profile based methods, Nearest Neighbor clustering, Connected Component Analysis, Cross Correlation, Radon Transform etc. Discussions on different standard databases of document image samples that have been used by the authors for experimentation purpose are included in the survey. These databases include NIST Federal Register Document Image Database, UW-I, UW-II, UW-III databases, ICDAR DISEC 2013 database, IFN/ENIT database etc. Here, we consider about eight such standard databases. Moreover, we compare different methods with respect to their accuracy and the respective range of skew angles detected by the concerned methods.

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Section: Other Methodsmentioning

confidence: 99%

An Overview of Existing Literature on Document Skew Detection

(Corresponding Author),

BHATTACHARYA,

CHAUDHURI

2023

MJCS

View full text Add to dashboard Cite

show abstract

“…We compare against two types of baselines. (i) Conventional image processing methods (Shukla et al, 2016;Pan et al, 2010;Veena M.N and Vasudev.T, 2015;Chinara et al, 2018;Vijayashree et al, 2014).…”

Section: Baselinesmentioning

confidence: 99%

“…( Shukla et al, 2016) 72.8 (Pan et al, 2010) 78.3 (Veena M. N and Vasudev.T, 2015) 74.5 (Chinara et al, 2018) 77.1 (Vijayashree et al, 2014) 76.3 (Channa andRao, 2020) 79.2 (Akhter and Rege, 2020) 81.4 MBSR-Net 99.9…”

Section: Model Hmean%mentioning

confidence: 99%

“…Research on text rotation prediction can be divided into two branches, the traditional Computer Vision methods based (Shukla et al, 2016;Pan et al, 2010;Veena M.N and Vasudev.T, 2015;Chinara et al, 2018;Vijayashree et al, 2014) and the deep neural networks based (Channa and Rao, 2020;Akhter and Rege, 2020). Shukala (Shukla et al, 2016) et al presented a Hough transform method to detect skews in licence plates.…”

Section: Introductionmentioning

confidence: 99%

“…Veena et al (Veena M.N and Vasudev.T, 2015) introduced a hybrid approach with combination of Autocorrelation and Radon transform to detect the orientation and skewed of segmented number plates from vehicle image. Chinara et al (Chinara et al, 2018) proposed to use Centre of Gravity (COG) method to predict the rotation of the images. Vijayashree et al (Vijayashree et al, 2014) proposed a method to first segment the input characters and then the inclination of the character to its base is estimated using line drawing algo- Though progress has been made in the text rotation prediction task, the state-of-the-art methods are far from being satisfactory.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Masked Bounding-Box Selection Based ResNet Predictor for Text Rotation Prediction

Yang¹,

Ye²,

Ho³

2022

Preprint

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The existing Optical Character Recognition (OCR) systems are capable of recognizing images with horizontal texts. However, when the rotation of the texts increases, it becomes harder to recognizing these texts. The performance of the OCR systems decreases. Thus predicting the rotations of the texts and correcting the images are important. Previous work mainly uses traditional Computer Vision methods like Hough Transform and Deep Learning methods like Convolutional Neural Network. However, all of these methods are prone to background noises commonly existing in general images with texts. To tackle this problem, in this work, we introduce a new masked bounding-box selection method, that incorporating the bounding box information into the system. By training a ResNet predictor to focus on the bounding box as the region of interest (ROI), the predictor learns to overlook the background noises. Evaluations on the text rotation prediction tasks show that our method improves the performance by a large margin.

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

Cited by 2 publications

References 7 publications

An Overview of Existing Literature on Document Skew Detection

An Overview of Existing Literature on Document Skew Detection

A Masked Bounding-Box Selection Based ResNet Predictor for Text Rotation Prediction

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