506 research outputs found
Learning-Based Arabic Word Spotting Using a Hierarchical Classifier
The effective retrieval of information from scanned and written documents is becoming essential with the increasing amounts of digitized documents, and therefore developing efficient means of analyzing and recognizing these documents is of significant interest. Among these methods is word spotting, which has recently become an active research area. Such systems have been implemented for Latin-based and Chinese languages, while few of them have been implemented for Arabic handwriting. The fact that Arabic writing is cursive by nature and unconstrained, with no clear white space between words, makes the processing of Arabic handwritten documents a more challenging problem.
In this thesis, the design and implementation of a learning-based Arabic handwritten word spotting system is presented. This incorporates the aspects of text line extraction, handwritten word recognition, partial segmentation of words, word spotting and finally validation of the spotted words.
The Arabic text line is more unconstrained than that of other scripts, essentially since it also includes small connected components such as dots and diacritics that are usually located between lines. Thus, a robust method to extract text lines that takes into consideration the challenges in the Arabic handwriting is proposed. The method is evaluated on two Arabic handwritten documents databases, and the results are compared with those of two other methods for text line extraction. The results show that the proposed method is effective, and compares favorably with the other methods.
Word spotting is an automatic process to search for words within a document. Applying this process to handwritten Arabic documents is challenging due to the absence of a clear space between handwritten words. To address this problem, an effective learning-based method for Arabic handwritten word spotting is proposed and presented in this thesis. For this process, sub-words or pieces of Arabic words form the basic components of the search process, and a hierarchical classifier is implemented to integrate statistical language models with the segmentation of an Arabic text line into sub-words.
The holistic and analytical paradigms (for word recognition and spotting) are studied, and verification models based on combining these two paradigms have been proposed and implemented to refine the outcomes of the analytical classifier that spots words.
Finally, a series of evaluation and testing experiments have been conducted to evaluate the effectiveness of the proposed systems, and these show that promising results have been obtained
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
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
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
Segmentation-free Word Spotting for Handwritten Arabic Documents
In this paper we present an unsupervised segmentation-free method for spotting and searching query, especially, for images documents in handwritten Arabic, for this, Histograms of Oriented Gradients (HOGs) are used as the feature vectors to represent the query and documents image. Then, we compress the descriptors with the product quantization method. Finally, a better representation of the query is obtained by using the Support Vector Machines (SVM)
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