Skew Detection in Binary Image Documents Based on Image Dilation and Region labeling Approach

Dhandra, B.V.; Malemath, V. S.; Mallikarjun, H M; Hegadi, Ravindra S.

doi:10.1109/icpr.2006.1060

Cited by 28 publications

(26 citation statements)

References 9 publications

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“…Compared to the algorithm [10][11] results are much better in the wider-angle region. If the proposed algorithm is used for final adjustment then text segmentation process is not needed.…”

Section: Discussionmentioning

confidence: 77%

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The Evaluation of the Initial Skew Rate for Printed Text

Brodić¹

2011

Journal of Electrical Engineering

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The Evaluation of the Initial Skew Rate for Printed TextIn this manuscript the algorithm for identification of the initial skew rate for printed text is presented. Proposed algorithm creates rectangular hull around all text characters. Combining nearby rectangular hulls form objects. After applying mathematical morphology on it, the biggest object is characterized as well as selected. Rectangular hull gravity center forms reference points on these objects used as a base for calculationieestimation of the initial skew rate. Using the least square method, initial skew rate is calculated. Comparative analysis of the origin and estimated skew rate is presented as well as discussed. Algorithm is examined with a number of printed text examples. Proposed algorithm showed robustness for skewness of printed text in the wide range.

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“…Compared to the algorithm [10][11] results are much better in the wider-angle region. If the proposed algorithm is used for final adjustment then text segmentation process is not needed.…”

Section: Discussionmentioning

confidence: 77%

“…Modification of the method [10] is presented in [11]. Proposed method advanced original method and solve some deficiencies.…”

Section: Introductionmentioning

confidence: 99%

The Evaluation of the Initial Skew Rate for Printed Text

Brodić¹

2011

Journal of Electrical Engineering

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“…B.V.Dhandra et.al [1] Proposes image dilation and region labeling technique for estimation of skew angle in a binary document image. The labeling of the region is done using depth first search.…”

Section: Techniques For Skew Detection and Correctionmentioning

confidence: 99%

“…Image dilation and region labeling [1], Hough transform [2], Connected components [4], Inter slice crosscorrelation [8], Content based predictors [9], Rectangular active contour [13], Nearest neighbor clustering [14] Rotation [9] NA…”

Section: Digitization and Storagementioning

confidence: 99%

A Language Independent Characterization of Document Image Noise in Historical Scripts

Sandhya¹,

Krishnan²,

Babu³

2012

IJCA

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Digitization of historical documents helps preserve these documents. As these documents have existed for a long time, various types of noise creep in. In our paper we have analyzed the different types of noise that occur in printed and handwritten historical documents mainly based on Kannada (Kannada is a language used in Karnataka, a southern state in India) documents and created a taxonomy for the same. We have also characterized each noise type based on factors such as their source, their effect on characters and the associated challenges in character recognition. We have also catalogued the different noise detection, removal and restoration techniques that are reported in the literature for each of the prominent noise types, and identified areas relating to noise detection, removal for further research focus.

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“…are removed using morphological opening. The next step in pre-processing is skew detection and correction and is performed using the algorithm [21].…”

Section: Data Collectionmentioning

confidence: 99%

Offline Handwritten Script Identification in Document Images

Hangarge¹,

Dhandra²

2010

IJCA

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Automatic handwritten script identification from document images facilitates many important applications such as sorting, transcription of multilingual documents and indexing of large collection of such images, or as a precursor to optical character recognition (OCR). In this paper, we investigate a texture as a tool for determining the script of handwritten document image, based on the observation that text has a distinct visual texture. Further, K nearest neighbour algorithm is used to classify 300 text blocks as well as 400 text lines into one of the three major Indian scripts: English, Devnagari and Urdu, based on 13 spatial spread features extracted using morphological filters. The proposed algorithm attains average classification accuracy as high as 99.2% for bi-script and 88.6% for tri-script separation at text line and text block level respectively with five fold cross validation test.

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Skew Detection in Binary Image Documents Based on Image Dilation and Region labeling Approach

Cited by 28 publications

References 9 publications

The Evaluation of the Initial Skew Rate for Printed Text

The Evaluation of the Initial Skew Rate for Printed Text

A Language Independent Characterization of Document Image Noise in Historical Scripts

Offline Handwritten Script Identification in Document Images

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