5,208 research outputs found
Text Line Segmentation of Historical Documents: a Survey
There is a huge amount of historical documents in libraries and in various
National Archives that have not been exploited electronically. Although
automatic reading of complete pages remains, in most cases, a long-term
objective, tasks such as word spotting, text/image alignment, authentication
and extraction of specific fields are in use today. For all these tasks, a
major step is document segmentation into text lines. Because of the low quality
and the complexity of these documents (background noise, artifacts due to
aging, interfering lines),automatic text line segmentation remains an open
research field. The objective of this paper is to present a survey of existing
methods, developed during the last decade, and dedicated to documents of
historical interest.Comment: 25 pages, submitted version, To appear in International Journal on
Document Analysis and Recognition, On line version available at
http://www.springerlink.com/content/k2813176280456k3
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
A Comparative study of Arabic handwritten characters invariant feature
This paper is practically interested in the unchangeable feature of Arabic
handwritten character. It presents results of comparative study achieved on
certain features extraction techniques of handwritten character, based on Hough
transform, Fourier transform, Wavelet transform and Gabor Filter. Obtained
results show that Hough Transform and Gabor filter are insensible to the
rotation and translation, Fourier Transform is sensible to the rotation but
insensible to the translation, in contrast to Hough Transform and Gabor filter,
Wavelets Transform is sensitive to the rotation as well as to the translation
Baseline Detection in Historical Documents using Convolutional U-Nets
Baseline detection is still a challenging task for heterogeneous collections
of historical documents. We present a novel approach to baseline extraction in
such settings, turning out the winning entry to the ICDAR 2017 Competition on
Baseline detection (cBAD). It utilizes deep convolutional nets (CNNs) for both,
the actual extraction of baselines, as well as for a simple form of layout
analysis in a pre-processing step. To the best of our knowledge it is the first
CNN-based system for baseline extraction applying a U-net architecture and
sliding window detection, profiting from a high local accuracy of the candidate
lines extracted. Final baseline post-processing complements our approach,
compensating for inaccuracies mainly due to missing context information during
sliding window detection. We experimentally evaluate the components of our
system individually on the cBAD dataset. Moreover, we investigate how it
generalizes to different data by means of the dataset used for the baseline
extraction task of the ICDAR 2017 Competition on Layout Analysis for
Challenging Medieval Manuscripts (HisDoc). A comparison with the results
reported for HisDoc shows that it also outperforms the contestants of the
latter.Comment: 6 pages, accepted to DAS 201
Handwritten Character Recognition of South Indian Scripts: A Review
Handwritten character recognition is always a frontier area of research in
the field of pattern recognition and image processing and there is a large
demand for OCR on hand written documents. Even though, sufficient studies have
performed in foreign scripts like Chinese, Japanese and Arabic characters, only
a very few work can be traced for handwritten character recognition of Indian
scripts especially for the South Indian scripts. This paper provides an
overview of offline handwritten character recognition in South Indian Scripts,
namely Malayalam, Tamil, Kannada and Telungu.Comment: Paper presented on the "National Conference on Indian Language
Computing", Kochi, February 19-20, 2011. 6 pages, 5 figure
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