492 research outputs found
The impact of the image processing in the indexation system
This paper presents an efficient word spotting system applied to handwritten Arabic documents, where images are represented with bag-of-visual-SIFT descriptors and a sliding window approach is used to locate the regions that are most similar to the query by following the query-by-example paragon. First, a pre-processing step is used to produce a better representation of the most informative features. Secondly, a region-based framework is deployed to represent each local region by a bag-of-visual-SIFT descriptors. Afterward, some experiments are in order to demonstrate the codebook size influence on the efficiency of the system, by analyzing the curse of dimensionality curve. In the end, to measure the similarity score, a floating distance based on the descriptor’s number for each query is adopted. The experimental results prove the efficiency of the proposed processing steps in the word spotting system
Combined cosine-linear regression model similarity with application to handwritten word spotting
The similarity or the distance measure have been used widely to calculate the similarity or dissimilarity between vector sequences, where the document images similarity is known as the domain that dealing with image information and both similarity/distance has been an important role for matching and pattern recognition. There are several types of similarity measure, we cover in this paper the survey of various distance measures used in the images matching and we explain the limitations associated with the existing distances. Then, we introduce the concept of the floating distance which describes the variation of the threshold’s selection for each word in decision making process, based on a combination of Linear Regression and cosine distance. Experiments are carried out on a handwritten Arabic image documents of Gallica library. These experiments show that the proposed floating distance outperforms the traditional distance in word spotting system
Deep Adaptive Learning for Writer Identification based on Single Handwritten Word Images
There are two types of information in each handwritten word image: explicit
information which can be easily read or derived directly, such as lexical
content or word length, and implicit attributes such as the author's identity.
Whether features learned by a neural network for one task can be used for
another task remains an open question. In this paper, we present a deep
adaptive learning method for writer identification based on single-word images
using multi-task learning. An auxiliary task is added to the training process
to enforce the emergence of reusable features. Our proposed method transfers
the benefits of the learned features of a convolutional neural network from an
auxiliary task such as explicit content recognition to the main task of writer
identification in a single procedure. Specifically, we propose a new adaptive
convolutional layer to exploit the learned deep features. A multi-task neural
network with one or several adaptive convolutional layers is trained
end-to-end, to exploit robust generic features for a specific main task, i.e.,
writer identification. Three auxiliary tasks, corresponding to three explicit
attributes of handwritten word images (lexical content, word length and
character attributes), are evaluated. Experimental results on two benchmark
datasets show that the proposed deep adaptive learning method can improve the
performance of writer identification based on single-word images, compared to
non-adaptive and simple linear-adaptive approaches.Comment: Under view of Pattern Recognitio
Cross-document word matching for segmentation and retrieval of Ottoman divans
Cataloged from PDF version of article.Motivated by the need for the automatic
indexing and analysis of huge number of documents in
Ottoman divan poetry, and for discovering new knowledge
to preserve and make alive this heritage, in this study we
propose a novel method for segmenting and retrieving
words in Ottoman divans. Documents in Ottoman are dif-
ficult to segment into words without a prior knowledge of
the word. In this study, using the idea that divans have
multiple copies (versions) by different writers in different
writing styles, and word segmentation in some of those
versions may be relatively easier to achieve than in other
versions, segmentation of the versions (which are difficult,
if not impossible, with traditional techniques) is performed
using information carried from the simpler version. One
version of a document is used as the source dataset and the
other version of the same document is used as the target
dataset. Words in the source dataset are automatically
extracted and used as queries to be spotted in the target
dataset for detecting word boundaries. We present the idea
of cross-document word matching for a novel task of
segmenting historical documents into words. We propose a
matching scheme based on possible combinations of
sequence of sub-words. We improve the performance of
simple features through considering the words in a context.
The method is applied on two versions of Layla and
Majnun divan by Fuzuli. The results show that, the proposed
word-matching-based segmentation method is
promising in finding the word boundaries and in retrieving
the words across documents
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
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