Document Content Extraction Using Automatically Discovered Features

Wang, Sui-Yu; Baird, Henry S.; Chang, An

doi:10.1109/icdar.2009.198

Cited by 7 publications

(6 citation statements)

References 20 publications

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“…Training on handwritten CCs and improved segmentation of touching characters and annotations (Fig. 8) would also likely improve precision/recall to match or exceed levels reported in [14] while competing with error rates reported in state-of-the-art approaches [18].…”

Section: Discussionmentioning

confidence: 92%

“…Automated selection of principle component features [18] could possibly increase precision while greatly reducing the dimensionality of the character space. Training on handwritten CCs and improved segmentation of touching characters and annotations (Fig.…”

Section: Discussionmentioning

confidence: 99%

“…Kmeans clustering followed by MRF relabeling is used in [14] for segmentation of handwriting, machine print and noise with an overall recall of 96.33%. In [18] a novel approach to automatically discover features pushes error rates of handwriting and machine print to 13.8%.…”

Section: Introductionmentioning

confidence: 99%

“…The weights are proportional to the standard deviation in the direction of the eigenvector. Solli also makes use of eigenfaces for looking up fonts in a large font library [18].…”

Section: Introductionmentioning

confidence: 99%

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Connected Component Level Discrimination of Handwritten and Machine-Printed Text Using Eigenfaces

Pinson¹,

Barrett

2011

2011 International Conference on Document Analysis and Recognition

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We employ Eigenfaces to discriminate between handwritten and machine-printed text at the connected component (CC) level. Normalized images of machine print CCs are treated as points in a high-dimensional space. PCA yields a reduceddimensional character space. Representative machine print CCs are projected into character space and a local distance threshold for each representative is automatically determined. CCs are classified as machine print if they are within the local distance threshold of their closest machine print representative. Otherwise, they are classified as handwriting. Recursive character segmentation using min graph cut is used to address the problem of touching characters. Validation over a large NIST handwriting and machine print database demonstrates precision of 93.98% and 89.1% for machine print and handwriting respectively.

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Section: Discussionmentioning

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The weights are proportional to the standard deviation in the direction of the eigenvector. Solli also makes use of eigenfaces for looking up fonts in a large font library [18].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Connected Component Level Discrimination of Handwritten and Machine-Printed Text Using Eigenfaces

Pinson¹,

Barrett

2011

2011 International Conference on Document Analysis and Recognition

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“…In Ref. 35, a novel approach to automatically discover features pushes error rates of handwriting and machine-printed text to 13.8%. Color annotation has been extracted from color documents in Ref.…”

Section: Introductionmentioning

confidence: 99%

Multiscale Region Projection Method to Discriminate Between Printed and Handwritten Text on Registration Forms

Tan

Lai

Wang

et al. 2015

Int. J. Patt. Recogn. Artif. Intell.

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Techniques to identify printed and handwritten text in scanned documents di®er signi¯cantly. In this paper, we address the question of how to discriminate between each type of writing on registration forms. Registration-form documents consist of various type zones, such as printed text, handwriting, table, image, noise, etc., so segmenting the various zones is a challenge. We adopt herein an approach called \ multiscale-region projection" to identify printed text and handwriting. An important aspect of our approach is the use of multiscale techniques to segment document images. A new set of projection features extracted from each zone is also proposed. The classi¯cation rules are mining and are used to discern printed text and table lines from handwritten text. The proposed system was tested on 11 118 samples in two registrationform-image databases. Some possible measures of e±ciency are computed, and in each case the proposed approach performs better than traditional methods.

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A comprehensive survey of mostly textual document segmentation algorithms since 2008

Eskenazi

Gomez‐Krämer

Ogier

2017

Pattern Recognition

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International audienceIn document image analysis, segmentation is the task that identifies the regions of a document. The increasing number of applications of document analysis requires a good knowledge of the available technologies. This survey highlights the variety of the approaches that have been proposed for document image segmentation since 2008. It provides a clear typology of documents and of document image segmentation algorithms. We also discuss the technical limitations of these algorithms, the way they are evaluated and the general trends of the community

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Document Content Extraction Using Automatically Discovered Features

Abstract: We

Cited by 7 publications

References 20 publications

Connected Component Level Discrimination of Handwritten and Machine-Printed Text Using Eigenfaces

Connected Component Level Discrimination of Handwritten and Machine-Printed Text Using Eigenfaces

Multiscale Region Projection Method to Discriminate Between Printed and Handwritten Text on Registration Forms

A comprehensive survey of mostly textual document segmentation algorithms since 2008

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