DOCUMENT IMAGE SKEW DETECTION: SURVEY AND ANNOTATED BIBLIOGRAPHY

1998 ◽  
pp. 40-64 ◽  
Author(s):  
JONATHAN J. HULL
Keyword(s):  
Document Image ◽  
Annotated Bibliography ◽  
Skew Detection

Skew Detection of Scanned Document Image Based on Shearlet Transform

2018 ◽  
Vol 55 (1) ◽  
pp. 011007
Author(s):  
张新红 Zhang Xinhong ◽  
张一凡 Zhang Yifan ◽  
张帆 Zhang Fan
Keyword(s):  
Document Image ◽  
Shearlet Transform ◽  
Skew Detection

A ROBUST SYSTEM FOR THRESHOLDING AND SKEW DETECTION IN MIXED TEXT/GRAPHICS DOCUMENTS

2005 ◽  
Vol 05 (02) ◽  
pp. 247-265 ◽  
Author(s):  
ADNAN AMIN ◽  
SUE WU
Keyword(s):  
Automatic System ◽  
Real Life ◽  
Detection Algorithm ◽  
Document Image ◽  
Superior Performance ◽  
Document Images ◽  
Skew Detection ◽  
Robust System ◽  
Graphic Images ◽  
Scanned Images

This article presents an automatic system that takes in grayscale scanned images, which could be mixed text/graphic documents, and performs thresholding and skew detection on the document images. The system consists of two major components; multistage thresholding and skew detection. The proposed skew detection algorithm has no restriction on detectable angle range and does not rely on large blocks of text. It works well on textual document images, graphical images and mixed text and graphic images. The performance of the systems was evaluated using over 60 images that consist of real life documents like envelopes and artificial mixed text/graphic icons. The superior performance of thresholding is clear compared to other techniques from the evaluation. The skew detection algorithm is robust when compared with other methods when very few text lines are present in the document image.

scholarly journals LANGUAGE INDEPENDENT ROBUST SKEW DETECTION AND CORRECTION TECHNIQUE FOR DOCUMENT IMAGES

2012 ◽  
pp. 111-115
Author(s):  
Neha. N
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Document Analysis ◽  
Document Image ◽  
Document Images ◽  
Novel Technique ◽  
Skew Detection ◽  
Optical Character ◽  
Correction Technique ◽  
All Optical

Document image processing is an increasingly important technology essential in all optical character recognition (OCR) systems and for automation of various office documents. A document originally has zero-skew (tilt), but when a page is scanned or photo copied, skew may be introduced due to various factors and is practically unavoidable. Presence even a small amount of skew (0.50) will have detrimental effects on document analysis as it has a direct effect on the reliability and efficiency of segmentation, recognition and feature extraction stages. Therefore removal of skew is of paramount importance in the field of document analysis and OCR and is the first step to be accomplished. This paper presents a novel technique for skew detection and correction which is both language and content independent. The proposed technique is based on the maximum density of the foreground pixels and their orientation in the document image. Unlike other conventional algorithms which work only for machine printed textual documents scripted in English, this technique works well for all kinds of document images (machine printed, hand written, complex, noisy and simple). The technique presented here is tested with 150 different document image samples and is found to provide results with an accuracy of 0.10

scholarly journals Skew detection based on vertical projection in latin character recognition of text document image

2018 ◽  
Vol 7 (4.44) ◽  
pp. 198
Author(s):  
Ronny Susanto ◽  
Farica P. Putri ◽  
Y. Widya Wiratama
Keyword(s):  
Error Rate ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Rotation Angle ◽  
Document Image ◽  
Computational Time ◽  
Vertical Projection ◽  
Word Error Rate ◽  
Skew Detection ◽  
Text Document

The accuracy of Optical Character Recognition is deeply affected by the skew of the image.  Skew detection & correction is one of the steps in OCR preprocessing to detect and correct the skew of document image. This research measures the effect of Combined Vertical Projection skew detection method to the accuracy of OCR. Accuracy of OCR is measured in Character Error Rate, Word Error Rate, and Word Error Rate (Order Independent). This research also measures the computational time needed in Combined Vertical Projection with different iteration. The experiment of Combined Vertical Projection is conducted by using iteration 0.5, 1, and 2 with rotation angle within -10 until 10 degrees. The experiment results show that the use of Combined Vertical Projection could lower the Character Error Rate, Word Error Rate, and Word Error Rate (Order Independent) up to 35.53, 34.51, and 32.74 percent, respectively. Using higher iteration value could lower the computational time but also decrease the accuracy of OCR.   

Maximizing projection grating slit for document image skew detection

2012 ◽  
Vol 31 (6) ◽  
pp. 1631-1633
Author(s):  
Fei ZHAO ◽  
Li-yang XIE ◽  
Jia LI
Keyword(s):  
Document Image ◽  
Skew Detection

scholarly journals Voting-Based Document Image Skew Detection

2020 ◽  
Vol 10 (7) ◽  
pp. 2236
Author(s):  
Costin-Anton Boiangiu ◽  
Ovidiu-Alexandru Dinu ◽  
Cornel Popescu ◽  
Nicolae Constantin ◽  
Cătălin Petrescu
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Large Scale ◽  
Document Image ◽  
Good Precision ◽  
Skew Angle ◽  
Skew Detection ◽  
Indispensable Tool ◽  
Technology Users ◽  
Image Page

Optical Character Recognition (OCR) is an indispensable tool for technology users nowadays, as our natural language is presented through text. We live under the need of having information at hand in every circumstance and, at the same time, having machines understand visual content and thus enable the user to be able to search through large quantities of text. To detect textual information and page layout in an image page, the latter must be properly oriented. This is the problem of the so-called document deskew, i.e., finding the skew angle and rotating by its opposite. This paper presents an original approach which combines various algorithms that solve the skew detection problem, with the purpose of always having at least one to compensate for the others’ shortcomings, so that any type of input document can be processed with good precision and solid confidence in the output result. The tests performed proved that the proposed solution is very robust and accurate, thus being suitable for large scale digitization projects.

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