Text Segmentation Ppt Nitheesha

8/10/2019 Text Segmentation Ppt Nitheesha

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SEMINAR BY:

NITHEESHA.S (1JB14LVS07)

UNDER THE GUIDENCE OF:

SOWMYA B.J

DEPT OF ECE

SJBIT BANGALORE


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With the wide use of networked equipment such as computers,

scanners, printers and copiers, it has become more important

to efficiently compress, store, and transfer large document

files.

For example, a typical color document scanned at 300 dpirequires approximately 24Mbytes of storage without

compression .


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Fig 1: MRC document compression standard mode 1 structure.


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Segmentation method is composed of two algorithms

that are applied in sequence:

The cost optimized segmentation (COS)algorithm

The connected component classification (CCC) algorithm.


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The COS algorithm is a block-based segmentation algorithm

based upon cost optimization.

The COS produces a binary image from a gray level or color

document; however, the resulting binary image typically

contains many false text detections.

The COS algorithms is comprised of two components:

Block wise segmentation and Global segmentation


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Fig 2: COS algorithm comprises two steps: blockwise segmentation and global

segmentation. The parameters of the cost function used in the global segmentation

are optimized in an offline training procedure.


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Fig 3. Illustration of a blockwise segmentation. The pixels in each block are separated

into foreground (1)or background (0)by comparing each pixel with a threshold t.

The threshold t is then selected to minimize the total subclass variance.


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Fig. b. Illustration of how thecomponent inversion step cancorrect erroneous segmentations

of text. (a) Original documentbefore segmentation. (b) Resultof COS binary segmentation. (c)Corrected segmentation aftercomponentinversion.


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The CCC algorithm further processes the resulting binary

image to improve the accuracy of the segmentation.

It does this by detecting nontext components (i.e., false text

detections) in a Bayesian framework which incorporates an

Markov random field (MRF) model of the component labels.


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The CCC algorithm proceeds in three steps:

connected component extraction,

component inversion,

component classification


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Fig. c. Illustration of a multiscale-COS/CCC algorithm. Segmentation progresses from

coarse to fine scales, incorporating the segmentation result from the previous coarser scale.

Both COS and CCC are performed on each scale, however only COS was adapted to the

multiscale scheme.


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A. Preprocessing

For consistency all scanner outputs were converted to sRGB

color coordinates [49] and descreened before segmentation . The scanned

RGB values were first converted to an intermediate device-independent colorspace, CIE XYZ, then transformed to sRGB .


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Fig. e. Text regions in the binary mask. The region is 165 370 pixels at 400 dpi, whichcorresponds to 1.04 cm 2.34 cm. (a) Original test image. (b) Ground truthsegmentation. (c) Otsu/CCC. (d) Multiscale-COS/CCC/Zheng. (e) DjVu. (f) LuraDocument.(g) COS. (h) COS/CCC. (i) Multiscale-COS/CCC.


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We presented a novel segmentation algorithm for the compression of rasterdocuments.

While the COS algorithm generates consistent initial segmentations,.

The CCC algorithm substantially reduces false detections through the use of acomponent-wise MRF context model.

We showed that the multiscale-COS/CCC algorithm achieves greater text detectionaccuracy with a lower false detection rate, as compared to state-of-the-art commercial

MRC products. Such text-only segmentations are also potentially useful for documentprocessing applications such as OCR.


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[1] ITU-T Recommendation T.44 Mixed Raster Content (MRC), T.44, InternationalTelecommunication Union, 1999.

[2] G. Nagy, S. Seth, and M. Viswanathan, A prototype document image

analysis system for technical journals, Computer, vol. 25, no. 7, pp.1022, 1992.

[3] K. Y.Wong and F. M.Wahl, Document analysis system, IBM J. Res.Develop., vol. 26, pp. 647656, 1982.

[4] J. Fisher, A rule-based system for document image segmentation, inProc. 10th Int. Conf. Pattern Recognit., 1990, pp. 567572.

[5] L. OGorman, The document spectrum for page layout analysis,IEEE Trans. Pattern Anal. Mach. Intell., vol. 15, no. 11, pp.

11621173, Nov. 1993.


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[6] Y. Chen and B. Wu, A multi-plane approach for text segmentationof complex document images, Pattern Recognit., vol. 42, no. 7, pp.14191444, 2009.

[7] R. C. Gonzalez and R. E. Woods, Digital Image Processing, 3rd ed.

Upper Saddle River, NJ: Pearson Education, 2008.

[8] F. Shafait, D. Keysers, and T. Breuel, Performance evaluation andbenchmarking of six-page segmentation algorithms, IEEE Trans. PatternAnal. Mach. Intell., vol. 30, no. 6, pp. 941954, Jun. 2008.

[9] K. Jung, K. Kim, and A. K. Jain, Text information extraction in images

and video: A survey, Pattern Recognit., vol. 37, no. 5, pp. 977

997,2004.

[10] G. Nagy, Twenty years of document image analysis in PAMI, IEEETrans. Pattern Anal. Mach. Intell., vol. 22, no. 1, pp. 3862, Jan. 2000


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Text Segmentation Ppt Nitheesha

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