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Timestamp: 2015-03-30 02:01:23
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Matched Legal Cases: ['Application No. 2007', 'Application No. 2007', 'Application No. 2007', 'Application No. 2007', 'Application No. 2007', 'Application No. 2007']

Patent US7876959 - Methods and systems for identifying text in digital images - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign inAdvanced Patent SearchPatentsAspects of the present invention relate to systems, methods and devices for detection of text in an image using an initial text classification result and a verification process. In particular, a support region of a candidate text pixel in a text-candidate map may be expanded to produce a revised text-candidate...http://www.google.com/patents/US7876959?utm_source=gb-gplus-sharePatent US7876959 - Methods and systems for identifying text in digital imagesAdvanced Patent SearchPublication numberUS7876959 B2Publication typeGrantApplication numberUS 11/470,519Publication dateJan 25, 2011Filing dateSep 6, 2006Priority dateSep 6, 2006Fee statusPaidAlso published asUS8150166, US20080056573, US20110110596Publication number11470519, 470519, US 7876959 B2, US 7876959B2, US-B2-7876959, US7876959 B2, US7876959B2InventorsToyohisa Matsuda, Richard John Campbell, Lawrence Shao-Hsien ChenOriginal AssigneeSharp Laboratories Of America, Inc.Export CitationBiBTeX, EndNote, RefManPatent Citations (99), Non-Patent Citations (43), Referenced by (4), Classifications (4), Legal Events (4) External Links: USPTO, USPTO Assignment, EspacenetMethods and systems for identifying text in digital images
US 7876959 B2Abstract
Aspects of the present invention relate to systems, methods and devices for detection of text in an image using an initial text classification result and a verification process. In particular, a support region of a candidate text pixel in a text-candidate map may be expanded to produce a revised text-candidate map. Pictorial regions in the image may be discriminated based on an entropy measure using masking and the revised text-candidate map, and the revised text-candidate map may be refined based on the pictorial regions.
N n x N n map , textCnt 32 and edgeCnt 33 for text 26 and edge 29, respectively. For example, for input one-bit maps of 600 dots-per-inch (dpi), an 8�8 summing operation will yield 75 dpi maps with entries ranging from 0 to 64 requiring 6 bits to represent each sum. In some embodiments, a sum of 0 and 1 may be represented by the same entry, therefore requiring only 5-bit maps.
textCandidateCnt textCandidateCnt + pictCandidateCut > TH 5 & edgeCnt > TH 6 & busyCnt > TH 7 on a pixel-by-pixel basis forming a revised text candidate map 46, designated textCandidateMap.
- ∑ i = 1 N h ( i ) * f ( h ( i ) ) , where N is the number of histogram bins, h(i) is the accumulation or count of bin i, and ∫(�) may be a function with mathematical characteristics similar to a logarithmic function. The entropy measure 75 may be weighted by the proportion of pixels that would have been counted in a bin, but were masked. The entropy measure is of the form:
- ∑ i = 1 N w ( i ) h ( i ) * f ( h ( i ) ) where w(i) is the weighting function. In some embodiments of the present invention, the function f(h(i)) may be log2(h(i)).
( ∑ i = 1 N h ( i ) = 1 ) histogram count for bin i, and log2(0)=1 may be defined for empty bins.
The entropy calculation may be transformed into fixed-point arithmetic to return an unsigned, 8-bit, uint 8, measured value, where zero corresponds to no entropy and 255 corresponds to maximum entropy. The fixed-point calculation may use two tables: one table to replace the logarithm calculation, denoted log_table below, and a second table to implement division in the histogram normalization step, denoted rev_table. Integer entropy calculation may be implemented as follows for an exemplary histogram with nine bins:
log_table  i  = 2 log_shift * log 2 ( i ) s = ∑ i = 0 8 hist [ i ] rev_table [ i ] = 2 rev_shift * 255 E max i s_log = log_table [ s ] s_rev = rev_table [ s ] bv [ i ] = hist [ i ] * s_rev log_diff [ i ] = s_log - log_table [ hist [ i ] ] E = ( ∑ i = 0 NBins ( ( bv [ i ] * log_diff [ i ] ) ⪢ ( log_shift + rev_shift - accum_shift ) ) ⪢ accum_shift where log_shift, rev_shift, and accum_shift may be related to the precision of the log, division, and accumulation operations, respectively.
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No. 11/424,297�Office action dated Oct. 22, 2009.Referenced byCiting PatentFiling datePublication dateApplicantTitleUS8077986 *Jun 4, 2007Dec 13, 2011Canon Kabushiki KaishaInformation processing apparatus, method of controlling same and computer programUS8452112 *Feb 3, 2009May 28, 2013Canon Kabushiki KaishaImage encoding apparatus and method of controlling the sameUS8571306Sep 9, 2011Oct 29, 2013Qualcomm IncorporatedCoding of feature location informationUS20090208125 *Feb 3, 2009Aug 20, 2009Canon Kabushiki KaishaImage encoding apparatus and method of controlling the same* Cited by examinerClassifications U.S. Classification382/176International ClassificationG06K9/36Cooperative ClassificationG06K9/00456European ClassificationG06K9/00L2Legal EventsDateCodeEventDescriptionJun 26, 2014FPAYFee paymentYear of fee payment: 4May 17, 2011CCCertificate of correctionMay 12, 2011ASAssignmentFree format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:SHARP LABORATORIES OF AMERICA, INC.;REEL/FRAME:026268/0330Effective date: 20110512Owner name: SHARP KABUSHIKI KAISHA, JAPANSep 6, 2006ASAssignmentOwner name: SHARP LABORATORIES OF AMERICA, INC., WASHINGTONFree format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:MATSUDA, TOYOHISA;CAMPBELL, RICHARD JOHN;CHEN, LAWRENCE SHAO-HSIEN;REEL/FRAME:018212/0218;SIGNING DATES FROM 20060904 TO 20060905Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:MATSUDA, TOYOHISA;CAMPBELL, RICHARD JOHN;CHEN, LAWRENCE SHAO-HSIEN;SIGNING DATES FROM 20060904 TO 20060905;REEL/FRAME:018212/0218RotateOriginal ImageGoogle Home - Sitemap - USPTO Bulk Downloads - Privacy Policy - Terms of Service - About Google Patents - Send FeedbackData provided by IFI CLAIMS Patent Services