2,877 research outputs found
READ-BAD: A New Dataset and Evaluation Scheme for Baseline Detection in Archival Documents
Text line detection is crucial for any application associated with Automatic
Text Recognition or Keyword Spotting. Modern algorithms perform good on
well-established datasets since they either comprise clean data or
simple/homogeneous page layouts. We have collected and annotated 2036 archival
document images from different locations and time periods. The dataset contains
varying page layouts and degradations that challenge text line segmentation
methods. Well established text line segmentation evaluation schemes such as the
Detection Rate or Recognition Accuracy demand for binarized data that is
annotated on a pixel level. Producing ground truth by these means is laborious
and not needed to determine a method's quality. In this paper we propose a new
evaluation scheme that is based on baselines. The proposed scheme has no need
for binarization and it can handle skewed as well as rotated text lines. The
ICDAR 2017 Competition on Baseline Detection and the ICDAR 2017 Competition on
Layout Analysis for Challenging Medieval Manuscripts used this evaluation
scheme. Finally, we present results achieved by a recently published text line
detection algorithm.Comment: Submitted to DAS201
Query by String word spotting based on character bi-gram indexing
In this paper we propose a segmentation-free query by string word spotting
method. Both the documents and query strings are encoded using a recently
proposed word representa- tion that projects images and strings into a common
atribute space based on a pyramidal histogram of characters(PHOC). These
attribute models are learned using linear SVMs over the Fisher Vector
representation of the images along with the PHOC labels of the corresponding
strings. In order to search through the whole page, document regions are
indexed per character bi- gram using a similar attribute representation. On top
of that, we propose an integral image representation of the document using a
simplified version of the attribute model for efficient computation. Finally we
introduce a re-ranking step in order to boost retrieval performance. We show
state-of-the-art results for segmentation-free query by string word spotting in
single-writer and multi-writer standard datasetsComment: To be published in ICDAR201
A line-based representation for matching words in historical manuscripts
Cataloged from PDF version of article.In this study, we propose a new method for retrieving and recognizing words in historical documents. We represent word images with a set of line segments. Then we provide a criterion for word matching based on matching the lines. We carry out experiments on a benchmark dataset consisting of manuscripts by George Washington, as well as on Ottoman manuscripts. (C) 2011 Elsevier B.V. All rights reserved
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