Text string extraction from images of colour-printed documents

Suen, H.-M.; Wang, J.-F.

doi:10.1049/ip-vis:19960325

Cited by 19 publications

(7 citation statements)

References 8 publications

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“…Edge-based methods were mainly used for color images or camera captured images [26] [27][28] [29] but not only [30] [31]. Some were also used as well with connected components analysis [32].…”

Section: Related Workmentioning

confidence: 99%

Text Extraction in Document Images: Highlight on Using Corner Points

Yadav

Ragot

2016

2016 12th IAPR Workshop on Document Analysis Systems (DAS)

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During past years, text extraction in document images has been widely studied in the general context of Document Image Analysis (DIA) and especially in the framework of layout analysis. Many existing techniques rely on complex processes based on preprocessing, image transforms or component/edges extraction and their analysis. At the same time, text extraction inside videos has received an increased interest and the use of corner or key points has been proven to be very effective. Because it is noteworthy to notice that very few studies were performed on the use of corner points for text extraction in document images, we propose in this paper to evaluate the possibilities associated with this kind of approach for DIA. To do that, we designed a very simple technique based on FAST key points. A first stage divide the image into blocks and the density of points inside each one is computed. The more dense ones are kept as text blocks. Then, connectivity of blocks is checked to group them and to obtain complete text blocks. This technique has been evaluated on different kind of images: different languages (Telugu, Arabic, French), handwritten as well as typewritten, skewed documents, images at different resolution and with different kind and amount of noises (deformations, ink dot, bleed through, acquisition (blur, resolution)), etc. Even with fixed parameters for all such kind of documents images, the precision and recall are close or higher to 90% which makes this basic method already effective. Consequently, even if the proposed approach does not propose a breakthrough from theoretical aspects, it highlights that accurate text extraction could be achieved without complex approach. Moreover, this approach could also be easily improved to be more precise, robust and useful for more complex layout analysis.

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Section: Related Workmentioning

confidence: 99%

Text Extraction in Document Images: Highlight on Using Corner Points

Yadav

Ragot

2016

2016 12th IAPR Workshop on Document Analysis Systems (DAS)

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“…For example, Zhong et al's verification stage assumed character size, the minimum number of characters in a text line, and horizontally aligned text lines [7]. Suen and Wang detected edges using a color edge detector [11]. The edges were grouped into rectangular blocks by a classic page segmentation technique RLSA (run length smoothing algorithm) [12].…”

Section: Review Of Previous Workmentioning

confidence: 99%

<title>Text extraction via an edge-bounded averaging and a parametric character model</title>

Fan

2003

SPIE Proceedings

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We present a text extraction algorithm that is deterministic and parametric. The algorithm relies on three basic assumptions about text: color/luminance uniformity of the interior region, closed boundaries of sharp edges and the consistency of local contrast. The algorithm is basically independent of the character alphabet, text layout, font size and orientation. The heart of this algorithm is an edge-bounded averaging for the classification of smooth regions that enhances robustness against noise without sacrificing boundary accuracy. We have also developed a verification process to clean up the residue of incoherent segmentation. Our framework provides a symmetric treatment for both regular and inverse text. We have proposed three heuristics for identifying the type of text from a cluster consisting of two types of pixel aggregates. Finally, we have demonstrated the advantages of the proposed algorithm over adaptive thresholding and block-based clustering methods in terms of boundary accuracy, segmentation coherency, and capability to identify inverse text and separate characters from background patches.

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“…The proposed technique relies on the fact that printed characters show strong regularity in shape, while handwritten text has no such characteristics [28,29]. Two assumptions have been made in the application of this technique: a) the skew angle problem has been solved; and b) the areas of printed and handwritten text are not overlapped.…”

Section: Handwritten Text Discriminationmentioning

confidence: 99%

An unconstrained handwriting recognition system

Kavallieratou¹,

Fakotakis²,

Kokkinakis

2002

International Journal on Document Analysis and Recognition

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Abstract. In this paper, an integrated offline recognition system for unconstrained handwriting is presented. The proposed system consists of seven main modules: skew angle estimation and correction, printedhandwritten text discrimination, line segmentation, slant removing, word segmentation, and character segmentation and recognition, stemming from the implementation of already existing algorithms as well as novel algorithms. This system has been tested on the NIST, IAM-DB, and GRUHD databases and has achieved accuracy that varies from 65.6% to 100% depending on the database and the experiment.

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Text string extraction from images of colour-printed documents

Cited by 19 publications

References 8 publications

Text Extraction in Document Images: Highlight on Using Corner Points

Text Extraction in Document Images: Highlight on Using Corner Points

<title>Text extraction via an edge-bounded averaging and a parametric character model</title>

An unconstrained handwriting recognition system

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