6,617 research outputs found
On-the-fly Historical Handwritten Text Annotation
The performance of information retrieval algorithms depends upon the
availability of ground truth labels annotated by experts. This is an important
prerequisite, and difficulties arise when the annotated ground truth labels are
incorrect or incomplete due to high levels of degradation. To address this
problem, this paper presents a simple method to perform on-the-fly annotation
of degraded historical handwritten text in ancient manuscripts. The proposed
method aims at quick generation of ground truth and correction of inaccurate
annotations such that the bounding box perfectly encapsulates the word, and
contains no added noise from the background or surroundings. This method will
potentially be of help to historians and researchers in generating and
correcting word labels in a document dynamically. The effectiveness of the
annotation method is empirically evaluated on an archival manuscript collection
from well-known publicly available datasets
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
Bridging the Semantic Gap in Multimedia Information Retrieval: Top-down and Bottom-up approaches
Semantic representation of multimedia information is vital for enabling the kind of multimedia search capabilities that professional searchers require. Manual annotation is often not possible because of the shear scale of the multimedia information that needs indexing. This paper explores the ways in which we are using both top-down, ontologically driven approaches and bottom-up, automatic-annotation approaches to provide retrieval facilities to users. We also discuss many of the current techniques that we are investigating to combine these top-down and bottom-up approaches
Automatic Palaeographic Exploration of Genizah Manuscripts
The Cairo Genizah is a collection of hand-written documents containing approximately
350,000 fragments of mainly Jewish texts discovered in the late 19th
century. The
fragments are today spread out in some 75 libraries and private collections worldwide,
but there is an ongoing effort to document and catalogue all extant fragments.
Palaeographic information plays a key role in the study of the Genizah collection.
Script style, and–more specifically–handwriting, can be used to identify fragments that
might originate from the same original work. Such matched fragments, commonly
referred to as “joins”, are currently identified manually by experts, and presumably only
a small fraction of existing joins have been discovered to date. In this work, we show
that automatic handwriting matching functions, obtained from non-specific features
using a corpus of writing samples, can perform this task quite reliably. In addition, we
explore the problem of grouping various Genizah documents by script style, without
being provided any prior information about the relevant styles. The automatically
obtained grouping agrees, for the most part, with the palaeographic taxonomy. In cases
where the method fails, it is due to apparent similarities between related scripts
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