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Proceedings ArticleDOI

Script based text identification: a multi-level architecture

TL;DR: The proposed framework presents a top-down approach by performing page, block/paragraph and word level script identification in multiple stages by utilizing texture and shape based information embedded in the documents at different levels for feature extraction.
Abstract: Script identification in a multi-lingual document environment has numerous applications in the field of document image analysis, such as indexing and retrieval or as an initial step towards optical character recognition. In this paper, we propose a novel hierarchical framework for script identification in bi-lingual documents. The framework presents a top-down approach by performing page, block/paragraph and word level script identification in multiple stages. We utilize texture and shape based information embedded in the documents at different levels for feature extraction. The prediction task at different levels of hierarchy is performed by Support Vector Machine (SVM) and Rejection based classifier defined using AdaBoost. Experimental evaluation of the proposed concept on document collections of Hindi/English and Bangla/English scripts have shown promising results.
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Book ChapterDOI
01 Jan 2022
TL;DR: In this article , a generic and integrated bilingual English-Hindi document classification system is proposed, which classifies heterogeneous documents using a dual class feeder and two character corpora.
Abstract: Today, rapid digitization requires efficient bilingual non-image and image document classification systems. Although many bilingual NLP and image-based systems provide solutions for real-world problems, they primarily focus on text extraction, identification, and recognition tasks with limited document types. This article discusses a journey of these systems and provides an overview of their methods, feature extraction techniques, document sets, classifiers, and accuracy for English-Hindi and other language pairs. The gaps found lead toward the idea of a generic and integrated bilingual English-Hindi document classification system, which classifies heterogeneous documents using a dual class feeder and two character corpora. Its non-image and image modules include pre- and post-processing stages and pre-and post-segmentation stages to classify documents into predefined classes. This article discusses many real-life applications on societal and commercial issues. The analytical results show important findings of existing and proposed systems.
Book ChapterDOI
01 Jan 2017
TL;DR: A robust script identification technique for 11 official handwritten Indic scripts namely, Bangla, Devanagari, Gujarati, Gurumukhi, Kannada, Malayalam, Manipuri, Oriya, Tamil, Telugu, Urdu along with Roman script is presented.
Abstract: As India is a multilingual country, hence, a variety of scripts are used here to write different languages. However, it becomes essential to recognize a particular script before the selection of an appropriate Optical Character Recognition (OCR) system. The research in this field is comparatively less explored and further research is required, particularly in the field of handwritten documents. This paper presents a robust script identification technique for 11 official handwritten Indic scripts namely, Bangla, Devanagari, Gujarati, Gurumukhi, Kannada, Malayalam, Manipuri, Oriya, Tamil, Telugu, Urdu along with Roman script. The recognition is performed at text-line level by using statistical textural features called Neighborhood Gray-Tone Difference Matrix along with Gray-level Run Length Matrix. The proposed method is experimented on a total dataset of 2400 handwritten text-lines of various scripts and yielded an identification rate of 97.69% using Multi Layer Perceptron (MLP) classifier.
References
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Journal ArticleDOI
TL;DR: In this paper, a face detection framework that is capable of processing images extremely rapidly while achieving high detection rates is described. But the detection performance is limited to 15 frames per second.
Abstract: This paper describes a face detection framework that is capable of processing images extremely rapidly while achieving high detection rates. There are three key contributions. The first is the introduction of a new image representation called the “Integral Image” which allows the features used by our detector to be computed very quickly. The second is a simple and efficient classifier which is built using the AdaBoost learning algorithm (Freund and Schapire, 1995) to select a small number of critical visual features from a very large set of potential features. The third contribution is a method for combining classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising face-like regions. A set of experiments in the domain of face detection is presented. The system yields face detection performance comparable to the best previous systems (Sung and Poggio, 1998; Rowley et al., 1998; Schneiderman and Kanade, 2000; Roth et al., 2000). Implemented on a conventional desktop, face detection proceeds at 15 frames per second.

13,037 citations

Proceedings ArticleDOI
07 Jul 2001
TL;DR: A new image representation called the “Integral Image” is introduced which allows the features used by the detector to be computed very quickly and a method for combining classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising face-like regions.
Abstract: This paper describes a face detection framework that is capable of processing images extremely rapidly while achieving high detection rates. There are three key contributions. The first is the introduction of a new image representation called the "Integral Image" which allows the features used by our detector to be computed very quickly. The second is a simple and efficient classifier which is built using the AdaBoost learning algo- rithm (Freund and Schapire, 1995) to select a small number of critical visual features from a very large set of potential features. The third contribution is a method for combining classifiers in a "cascade" which allows back- ground regions of the image to be quickly discarded while spending more computation on promising face-like regions. A set of experiments in the domain of face detection is presented. The system yields face detection perfor- mance comparable to the best previous systems (Sung and Poggio, 1998; Rowley et al., 1998; Schneiderman and Kanade, 2000; Roth et al., 2000). Implemented on a conventional desktop, face detection proceeds at 15 frames per second.

10,592 citations

BookDOI
01 Dec 2001
TL;DR: Learning with Kernels provides an introduction to SVMs and related kernel methods that provide all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms.
Abstract: From the Publisher: In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs—-kernels--for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics. Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.

7,880 citations

Journal ArticleDOI
TL;DR: Rotation invariant texture features are computed based on an extension of the popular multi-channel Gabor filtering technique, and their effectiveness is tested with 300 randomly rotated samples of 15 Brodatz textures to solve a practical but hitherto mostly overlooked problem in document image processing.
Abstract: Concerns the extraction of rotation invariant texture features and the use of such features in script identification from document images Rotation invariant texture features are computed based on an extension of the popular multi-channel Gabor filtering technique, and their effectiveness is tested with 300 randomly rotated samples of 15 Brodatz textures These features are then used in an attempt to solve a practical but hitherto mostly overlooked problem in document image processing-the identification of the script of a machine printed document Automatic script and language recognition is an essential front-end process for the efficient and correct use of OCR and language translation products in a multilingual environment Six languages (Chinese, English, Greek, Russian, Persian, and Malayalam) are chosen to demonstrate the potential of such a texture-based approach in script identification

293 citations