Efficient Skew Detection and Correction in Scanned Document Images Through Clustering of Probabilistic Hough Transforms

2021 ◽  
Author(s):  
Riaz Ahmad ◽  
Saeeda Naz ◽  
Imran Razzak
Keyword(s):  
Document Images ◽  
Skew Detection ◽  
Hough Transforms

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

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