DocumentCode
3489986
Title
OCR-Free Transcript Alignment
Author
Hassner, Tal ; Wolf, Lars ; Dershowitz, Nachum
Author_Institution
Dept. of Math. & Comput. Sci., Open Univ., Raanana, Israel
fYear
2013
fDate
25-28 Aug. 2013
Firstpage
1310
Lastpage
1314
Abstract
Recent large-scale digitization and preservation efforts have made images of original manuscripts, accompanied by transcripts, commonly available. An important challenge, for which no practical system exists, is that of aligning transcript letters to their coordinates in manuscript images. Here we propose a system that directly matches the image of a historical text with a synthetic image created from the transcript for the purpose. This, rather than attempting to recognize individual letters in the manuscript image using optical character recognition (OCR). Our method matches the pixels of the two images by employing a dedicated dense flow mechanism coupled with novel local image descriptors designed to spatially integrate local patch similarities. Matching these pixel representations is performed using a message passing algorithm. The various stages of our method make it robust with respect to document degradation, to variations between script styles and to non-linear image transformations. Robustness, as well as practicality of the system, are verified by comprehensive empirical experiments.
Keywords
document image processing; image matching; image representation; message passing; optical character recognition; text detection; OCR-free transcript alignment; dedicated dense flow mechanism; document degradation; historical text; image matching; individual letter recognition; large-scale digitization; local image descriptors; local patch similarities; manuscript images; message passing algorithm; nonlinear image transformations; optical character recognition; pixel representation matching; preservation; script style variation; synthetic image; Conferences; Face; Face recognition; Histograms; Optical character recognition software; Optical imaging;
fLanguage
English
Publisher
ieee
Conference_Titel
Document Analysis and Recognition (ICDAR), 2013 12th International Conference on
Conference_Location
Washington, DC
ISSN
1520-5363
Type
conf
DOI
10.1109/ICDAR.2013.265
Filename
6628826
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