Reading newspaper text

Lam, Stephen W.; Wang, D.; Srihari, Sargur N.

doi:10.1109/icpr.1990.118197

Cited by 14 publications

(6 citation statements)

References 5 publications

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“…Text extraction methods are classified as threshold based and grouping based. Threshold based text extraction includes histogram based on Thresholding [8], adaptive or local Thresholding [9] and entropy based methods. Grouping based method encompasses clustering based, learning and region based methods.…”

Section: Texture Based Methodsmentioning

confidence: 99%

Text Extraction of Vehicle Number Plate and Document Images Using Discrete Wavelet Transform in MATLAB

Goel¹,

Sharma²

2014

IOSRJCE

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Section: Texture Based Methodsmentioning

confidence: 99%

Text Extraction of Vehicle Number Plate and Document Images Using Discrete Wavelet Transform in MATLAB

Goel¹,

Sharma²

2014

IOSRJCE

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“…Then, meaningful features are computed in these regions and a linear adaptive classification scheme is constructed to discriminate text regions from others. The Lam's et al study employed a method which segments a newspaper document image into labeled macro zones and filters connected components to determine the content of the zones as text or nontext [18]. The drawback of [17] and [18] was that segmentation was achieved upon the assumption that the document image consists of rectangular areas.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The Lam's et al study employed a method which segments a newspaper document image into labeled macro zones and filters connected components to determine the content of the zones as text or nontext [18]. The drawback of [17] and [18] was that segmentation was achieved upon the assumption that the document image consists of rectangular areas. Antonacopoulos et al proposed a technique that used the structure of the background white space, surrounded by the printed zones [19].…”

Section: Literature Reviewmentioning

confidence: 99%

Page layout analysis and classification for complex scanned documents

et al. 2011

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understand that I must submit a print copy of my thesis or dissertation to the RIT Archives, per current RIT guidelines for the completion of my degree. I hereby grant to the Rochester Institute of Technology and its agents the non-exclusive license to archive and make accessible my thesis or dissertation in whole or in part in all forms of media in perpetuity. I retain all other ownership I would like to acknowledge and extend my heartfelt gratitude to the following colleagues: Mustafa Jaber for sharing his valuable experiences, and always being optimistic, Abdul Haleem Syed for his positive outlook on life and for our worthwhile discussions. I am also thankful to Mr. Peter Bauer, and the Hewlett Packard Company for their support and sponsorship for making this research possible. I am very grateful to Dr. Drew Maywar who helped me discover a new world in electrical engineering. His knowledge and vision changed my point of view, and provided me with a new focus in an area of study which fascinates me. I also would like to thank to Dr. Ferat Sahin and Prof. Sohail Dianat for agreeing to be committee members for my thesis, and sharing their valuable ideas and reviews. And finally, special thanks to Prof. Eli Saber who gave me an opportunity to work on his exciting projects. This thesis could not have been written without his encouragement throughout my academic life at RIT. His exacting character motivated me to aspire to greater academic achievements. He never accepted less than my best efforts, as he well knows. In addition to his academic support, he has always been there and willing to discuss my concerns, even if they are very personal. He taught me the significance of pushing myself, trying hard, and preserving. Thank you Prof. Eli Saber for everything, it has been a pleasure and an honor being your student.

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“…RqEwxflPc (5) In equation (5), Rq is the image recognition quality measure, P2 is the word edit distance from the true result, and P is a character confidence value returned from the character recognition classifier. E itself incorporates a measure of the number of insertions, deletions and substitutions necessary to transform the recognition string to the truth string.…”

Section: Experiments Descriptionmentioning

confidence: 99%

“…The physical size of each block as well as its location in the document image can vaiy widely. The major components that are typically present are: a bounding frame around the check, a remitter name and address block (1), a payee name and address block (4), the legal amount for which the check can be cashed (2), the courtesy amount (3), and a signature (5).…”

Section: Geometric Structure Analysismentioning

confidence: 99%

<title>Image-understanding approach to reading business transaction documents</title>

Oddo¹,

McNamara²,

Smith³

1994

SPIE Proceedings

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One high-payoff application of Image Character Recognition (ICR) technology is the automatic processing of business transaction documents. For remittance types of transactions, these documents consist of checks and invoices. While the general layout of a check is consistent with respect to types of data fields present, there is variability in the locations of the data fields printed on the document. Furthermore, checks are printed using multiple fonts, character sizes, and line spacing on a single document. In contrast, invoice contents and layouts vaiy widely from business to business. The high degree of layout variability of business documents poses significant problems with respect to data field location and extraction in preparation for ICR. This paper describes an image understanding approach for locating and extracting data fields from various business documents. This approach is shown to be tolerant to image rotation, multiple fonts, multiple character sizes, and line spacing. Actual checks and invoices are processed using the described algorithms. The resulting field isolation and extraction perfonnance, together with the resulting ICR read rates, are statistically analyzed and presented.

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Reading newspaper text

Cited by 14 publications

References 5 publications

Text Extraction of Vehicle Number Plate and Document Images Using Discrete Wavelet Transform in MATLAB

Text Extraction of Vehicle Number Plate and Document Images Using Discrete Wavelet Transform in MATLAB

Page layout analysis and classification for complex scanned documents

<title>Image-understanding approach to reading business transaction documents</title>

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