Efficient skew estimation and correction algorithm for document images

This paper presents an approach to evaluate the quality of handwritten words using the set of features in the preprocessing stages. This is to determine the effect of various stages of preprocessing of the recognition of Yoruba handwritten characters. We demonstrate our methods using handwritten words samples of the domain of Yoruba medical terminology collected from indigenous Yoruba literate writers. Samples were digitized at 300dpi to facilitate much detail representation of the image dataset. We study the impact of entropy measure for an intuitive interpretation of the analysis of the handwritten words. From the experiment carried out, it was observed that the entropy measure of handwritten word is higher than the typewritten word. This implies that the Information content of the handwritten word is affected by perturbations which need to be removed using appropriate image preprocessing tools to obtain low entropy measure which implies same information content as the original. General TermsPattern Recognition, Handwritten word recognition, Image preprocessing and Information Theory.

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“…A class of normalization processes is described by [25] with the following relations between I 1 and I 2 as in equation (5).…”

Section: Fig 1 a Generic Image Preprocessing Toolsmentioning

confidence: 99%

“…Skew correction techniques can be categorized into direct, indirect and contour-oriented [25]. Equation (3) shows the direct method.…”

Section: Fig 1 a Generic Image Preprocessing Toolsmentioning

confidence: 99%

Yoruba Handwriting Word Recognition Quality Evaluation of Preprocessing Attributes using Information Theory Approach

F.Ajao¹,

O.Olabiyisi²,

O.Omidiora³

et al. 2015

IJAIS

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“…One technique of skew estimation considers the projection profile of the document; another class of approach is based on nearest neighbor clustering of connected components. Literatures exist for skew detection and correction in [14][15][16][17].…”

Section: Skew Detection and Correctionmentioning

confidence: 99%

Assamese Digit Recognition with Feed Forward Neural Network

Medhi¹,

Kalita²

2015

IJCA

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The aim of this paper is to design a recognizer to recognize Assamese digits using feed forward neural network. The recognizer crops the individual digits of the image using bounding box method and extracts the feature. In the present study zoning is used to obtain necessary feature vector. This feature is provided as input to the classifier and the network is trained with backpropagation training algorithm with two hidden layer. The recognition rate of printed digits is 98%, including multi size, bold and italics fonts. In case of handwritten digits recognition rate is 70.6%.

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“…The majority of them are based on projection profile (PP) [1,2], Hough transform (HT) [3][4][5], and nearest neighbor clustering (NNC) algorithms [6,7]. A comprehensive survey of skew detection methods refers to Ref.…”

Section: Introductionmentioning

confidence: 99%

“…The accuracy of PP method depends on the angular resolution of the projection profile, and it is easily affected by the noise and the character fragments in an images. The method based on Hough transform (HT) was proposed by Srihari et al (1989) [3] and Hinds et al (1990) [4]. After mapping each point from the original plane to all points in the Hough space, the maximum number of colinear pixels in Hough space is on lines that are coincident with the baseline of the text.…”

Section: Introductionmentioning

confidence: 99%

Skew detection in document images based on rectangular active contour

Fan

Zhu

Tang

2010

IJDAR

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The digitalization processes of documents produce frequently images with small rotation angles. The skew angles in document images degrade the performance of optical character recognition (OCR) tools. Therefore, skew detection of document images plays an important role in automatic document analysis systems. In this paper, we propose a Rectangular Active Contour Model (RAC Model) for content region detection and skew angle calculation by imposing a rectangular shape constraint on the zero-level set in ChanVese Model (C-V Model) according to the rectangular feature of content regions in document images. Our algorithm differs from other skew detection methods in that it does not rely on local image features. Instead, it uses global image features and shape constraint to obtain a strong robustness in detecting skew angles of document images. We experimented on different types of document images. Comparing the results with other skew detection algorithms, our algorithm is more accurate in detecting the skews of the complex document images with different fonts, tables, illustrations, and layouts. We do not need to pre-process the original image, even if it is noisy, and at the same time the rectangular content region of a document image is also detected.

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Efficient skew estimation and correction algorithm for document images

Cited by 32 publications

References 11 publications

Yoruba Handwriting Word Recognition Quality Evaluation of Preprocessing Attributes using Information Theory Approach

Yoruba Handwriting Word Recognition Quality Evaluation of Preprocessing Attributes using Information Theory Approach

Assamese Digit Recognition with Feed Forward Neural Network

Skew detection in document images based on rectangular active contour

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