The Experts below are selected from a list of 53793 Experts worldwide ranked by ideXlab platform
Jun Guo - One of the best experts on this subject based on the ideXlab platform.
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Text Extraction from natural scene image: A survey
Neurocomputing, 2013Co-Authors: X F Zhang, Yi Zhe Song, Kaili Zhao, Honggang Zhang, Jun GuoAbstract:Abstract With the increasing popularity of portable camera devices and embedded visual processing, Text Extraction from natural scene images has become a key problem that is deemed to change our everyday lives via novel applications such as augmented reality. Text Extraction from natural scene images algorithms is generally composed of the following three stages: (i) detection and localization, (ii) Text enhancement and segmentation and (iii) optical character recognition (OCR). The problem is challenging in nature due to variations in the font size and color, Text alignment, illumination change and reflections. This paper aims to classify and assess the latest algorithms. More specifically, we draw attention to studies on the first two steps in the Extraction process, since OCR is a well-studied area where powerful algorithms already exist. This paper offers to the researchers a link to public image database for the algorithm assessment of Text Extraction from natural scene images.
Keechul Jung - One of the best experts on this subject based on the ideXlab platform.
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Text information Extraction in images and video a survey
Pattern Recognition, 2004Co-Authors: Keechul Jung, Kwang In Kim, Anil K JainAbstract:Text data present in images and video contain useful information for automatic annotation, indexing, and structuring of images. Extraction of this information involves detection, localization, tracking, Extraction, enhancement, and recognition of the Text from a given image. However, variations of Text due to differences in size, style, orientation, and alignment, as well as low image contrast and complex background make the problem of automatic Text Extraction extremely challenging. While comprehensive surveys of related problems such as face detection, document analysis, and image & video indexing can be found, the problem of Text information Extraction is not well surveyed. A large number of techniques have been proposed to address this problem, and the purpose of this paper is to classify and review these algorithms, discuss benchmark data and performance evaluation, and to point out promising directions for future research.
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neural network based Text location in color images
Pattern Recognition Letters, 2001Co-Authors: Keechul JungAbstract:Abstract This paper proposes neural network-based Text locations in complex color images. Texture information extracted on several color bands using neural networks is combined and corresponding Text location algorithms are then developed. Text Extraction filters can be automatically constructed using neural networks. Comparisons with other Text location methods are presented; indicating that the proposed system has a better accuracy.
Graciela Gonzalez - One of the best experts on this subject based on the ideXlab platform.
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banner an executable survey of advances in biomedical named entity recognition
Pacific Symposium on Biocomputing, 2007Co-Authors: Robert Leaman, Graciela GonzalezAbstract:There has been an increasing amount of research on biomedical named entity recognition, t he most basic Text Extraction problem, resulting in significant progress by different research teams around the world. This has created a need for a freely-available, open source system implementing the advances described in the literature. In this paper we present BANNER, an open-source, executable survey of advances in biomedical named entity recognition, intended to serve as a benchmark for the field. BANNER is implemented in Java as a machine-learning system based on conditional random fields and includes a wide survey of the best techniques recently described in the literature. It is designed to maximize domain independence by not employing brittle semantic features or rule-based processing steps, and achieves significantly better performance than existing baseline systems. It is therefore useful to developers as an extensible NER implementation, to researchers as a standard for comparing innovative techniques, and to biologists requiring the ability to find novel entities in large amounts of Text. BANNER is available for download at http://banner.sourceforge.net.
X F Zhang - One of the best experts on this subject based on the ideXlab platform.
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Text Extraction from natural scene image: A survey
Neurocomputing, 2013Co-Authors: X F Zhang, Yi Zhe Song, Kaili Zhao, Honggang Zhang, Jun GuoAbstract:Abstract With the increasing popularity of portable camera devices and embedded visual processing, Text Extraction from natural scene images has become a key problem that is deemed to change our everyday lives via novel applications such as augmented reality. Text Extraction from natural scene images algorithms is generally composed of the following three stages: (i) detection and localization, (ii) Text enhancement and segmentation and (iii) optical character recognition (OCR). The problem is challenging in nature due to variations in the font size and color, Text alignment, illumination change and reflections. This paper aims to classify and assess the latest algorithms. More specifically, we draw attention to studies on the first two steps in the Extraction process, since OCR is a well-studied area where powerful algorithms already exist. This paper offers to the researchers a link to public image database for the algorithm assessment of Text Extraction from natural scene images.
Yingli Tian - One of the best experts on this subject based on the ideXlab platform.
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scene Text recognition in mobile applications by character descriptor and structure configuration
IEEE Transactions on Image Processing, 2014Co-Authors: Yingli TianAbstract:Text characters and strings in natural scene can provide valuable information for many applications. Extracting Text directly from natural scene images or videos is a challenging task because of diverse Text patterns and variant background interferences. This paper proposes a method of scene Text recognition from detected Text regions. In Text detection, our previously proposed algorithms are applied to obtain Text regions from scene image. First, we design a discriminative character descriptor by combining several state-of-the-art feature detectors and descriptors. Second, we model character structure at each character class by designing stroke configuration maps. Our algorithm design is compatible with the application of scene Text Extraction in smart mobile devices. An Android-based demo system is developed to show the effectiveness of our proposed method on scene Text information Extraction from nearby objects. The demo system also provides us some insight into algorithm design and performance improvement of scene Text Extraction. The evaluation results on benchmark data sets demonstrate that our proposed scheme of Text recognition is comparable with the best existing methods.
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detecting good quality frames in videos captured by a wearable camera for blind navigation
Bioinformatics and Biomedicine, 2013Co-Authors: Long Tian, Yingli TianAbstract:Recent technology developments in computer vision, digital cameras, and portable computers make it possible to assist blind individuals by developing camera-based object recognition products. However, motion blur caused by a moving camera limits the real-world application of wayfinding for blind users. In this paper, we propose a new method to detect good quality frames from videos captured by cameras, which are taken by blind users. In our proposed method, both gradient and intensity statistics are extracted from video frames. Then a support vector machine (SVM) based classifier is applied to identify the frames with good quality (Unblurred) from those blurred frames. The Unblurred frames will be further processed to extract essential information for blind wayfinding and navigation such as signage recognition and Text Extraction. Experimental results demonstrate that our proposed method is able to robustly handle video motions in both indoor and outdoor environments.
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Text Extraction from scene images by character appearance and structure modeling
Computer Vision and Image Understanding, 2013Co-Authors: Yingli TianAbstract:In this paper, we propose a novel algorithm to detect Text information from natural scene images. Scene Text classification and detection are still open research topics. Our proposed algorithm is able to model both character appearance and structure to generate representative and discriminative Text descriptors. The contributions of this paper include three aspects: (1) a new character appearance model by a structure correlation algorithm which extracts discriminative appearance features from detected interest points of character samples; (2) a new Text descriptor based on structons and correlatons, which model character structure by structure differences among character samples and structure component co-occurrence; and (3) a new Text region localization method by combining color decomposition, character contour refinement, and string line alignment to localize character candidates and refine detected Text regions. We perform three groups of experiments to evaluate the effectiveness of our proposed algorithm, including Text classification, Text detection, and character identification. The evaluation results on benchmark datasets demonstrate that our algorithm achieves the state-of-the-art performance on scene Text classification and detection, and significantly outperforms the existing algorithms for character identification.
