Publikation
An Image Based Performance Evaluation Method for Page Dewarping Algorithms using SIFT Features
Syed Saqib Bukhari; Faisal Shafait; Thomas Breuel
In: 4th International Workshop on Camera-Based Document Analysis and Recognition. International Workshop on Camera-Based Document Analysis and Recognition (CBDAR-11), 4th, September 22, Beijing, China, Lecture Notes in Computer Science (LNCS), Springer, 9/2011.
Zusammenfassung
Dewarping of camera-captured document images
is one the important preprocessing steps before feeding them
to a document analysis system. Over the last few years, many
approaches have been proposed for document image dewarping.
Usually optical character recognition (OCR) based and/or
feature based approaches are used for the evaluation of dewarping
algorithms. OCR based evaluation is a good measure
for the performance of a dewarping method on text regions, but
it does not measure how well the dewarping algorithm works
on the non-text regions like mathematical equations, graphics,
or tables. Feature based evaluation methods, on the other
hand, do not have this problem, however, they have following
limitations: i) a lot of manual assistance is required for groundtruth
generation, and ii) evaluation metrics are not sufficient
to get meaningful information about dewarping quality. In
this paper, we present an image based methodology for the
performance evaluation of dewarping algorithms using SIFT
features. For ground-truths, our method only requires scanned
images of pages which have been captured by a camera.
This paper introduces a vectorial performance evaluation score
which gives comprehensive information for determining the
performance of different dewarping methods. We have tested
our performance evaluation methodology on the participating
methods of CBDAR 2007 document image dewarping contest
and illustrated the correctness of our method.