Bio: Bolan Su is an academic researcher from Institute for Infocomm Research Singapore. The author has contributed to research in topics: Image segmentation & Image restoration. The author has an hindex of 18, co-authored 31 publications receiving 2020 citations. Previous affiliations of Bolan Su include National University of Singapore & Agency for Science, Technology and Research.
TL;DR: A novel document image binarization technique that addresses issues ofSegmentation of text from badly degraded document images by using adaptive image contrast, a combination of the local image contrast and theLocal image gradient that is tolerant to text and background variation caused by different types of document degradations.
Abstract: Segmentation of text from badly degraded document images is a very challenging task due to the high inter/intra-variation between the document background and the foreground text of different document images. In this paper, we propose a novel document image binarization technique that addresses these issues by using adaptive image contrast. The adaptive image contrast is a combination of the local image contrast and the local image gradient that is tolerant to text and background variation caused by different types of document degradations. In the proposed technique, an adaptive contrast map is first constructed for an input degraded document image. The contrast map is then binarized and combined with Canny's edge map to identify the text stroke edge pixels. The document text is further segmented by a local threshold that is estimated based on the intensities of detected text stroke edge pixels within a local window. The proposed method is simple, robust, and involves minimum parameter tuning. It has been tested on three public datasets that are used in the recent document image binarization contest (DIBCO) 2009 & 2011 and handwritten-DIBCO 2010 and achieves accuracies of 93.5%, 87.8%, and 92.03%, respectively, that are significantly higher than or close to that of the best-performing methods reported in the three contests. Experiments on the Bickley diary dataset that consists of several challenging bad quality document images also show the superior performance of our proposed method, compared with other techniques.
••09 Jun 2010
TL;DR: A new document image binarization technique that segments the text from badly degraded historical document images by using local thresholds that are estimated from the detected high contrast pixels within a local neighborhood window.
Abstract: This paper presents a new document image binarization technique that segments the text from badly degraded historical document images. The proposed technique makes use of the image contrast that is defined by the local image maximum and minimum. Compared with the image gradient, the image contrast evaluated by the local maximum and minimum has a nice property that it is more tolerant to the uneven illumination and other types of document degradation such as smear. Given a historical document image, the proposed technique first constructs a contrast image and then detects the high contrast image pixels which usually lie around the text stroke boundary. The document text is then segmented by using local thresholds that are estimated from the detected high contrast pixels within a local neighborhood window. The proposed technique has been tested over the dataset that is used in the recent Document Image Binarization Contest (DIBCO) 2009. Experiments show its superior performance.
TL;DR: The proposed technique was submitted to the recent document image binarization contest (DIBCO) held under the framework of ICDAR 2009 and has achieved the top performance among 43 algorithms that are submitted from 35 international research groups.
Abstract: Document images often suffer from different types of degradation that renders the document image binarization a challenging task. This paper presents a document image binarization technique that segments the text from badly degraded document images accurately. The proposed technique is based on the observations that the text documents usually have a document background of the uniform color and texture and the document text within it has a different intensity level compared with the surrounding document background. Given a document image, the proposed technique first estimates a document background surface through an iterative polynomial smoothing procedure. Different types of document degradation are then compensated by using the estimated document background surface. The text stroke edge is further detected from the compensated document image by using L1-norm image gradient. Finally, the document text is segmented by a local threshold that is estimated based on the detected text stroke edges. The proposed technique was submitted to the recent document image binarization contest (DIBCO) held under the framework of ICDAR 2009 and has achieved the top performance among 43 algorithms that are submitted from 35 international research groups.
••01 Nov 2014
TL;DR: This paper presents a novel approach to recognize text in scene images that outperforms the state-of-the-art techniques significantly and is able to recognize the whole word images without character-level segmentation and recognition.
Abstract: Scene text recognition is a useful but very challenging task due to uncontrolled condition of text in natural scenes. This paper presents a novel approach to recognize text in scene images. In the proposed technique, a word image is first converted into a sequential column vectors based on Histogram of Oriented Gradient (HOG). The Recurrent Neural Network (RNN) is then adapted to classify the sequential feature vectors into the corresponding word. Compared with most of the existing methods that follow a bottom-up approach to form words by grouping the recognized characters, our proposed method is able to recognize the whole word images without character-level segmentation and recognition. Experiments on a number of publicly available datasets show that the proposed method outperforms the state-of-the-art techniques significantly. In addition, the recognition results on publicly available datasets provide a good benchmark for the future research in this area.
01 Jan 2007
TL;DR: In this paper, a brief overview of the current development and application status of employing electrospun composite nanofi brous for constructing biomimetic and bioactive tissue scaffolds is given.
Abstract: Electrospinning is an enabling technology that can architecturally (in terms of geometry, morphology or topography) and biochemically fabricate engineered cellular scaffolds that mimic the native extracellular matrix (ECM). This is especially important and forms one of the essential paradigms in the area of tissue engineering. While biomimesis of the physical dimensions of native ECM's major constituents (eg, collagen) is no longer a fabrication-related challenge in tissue engineering research, conveying bioactivity to electrospun nanofi brous structures will determine the effi ciency of utilizing electrospun nanofi bers for regenerating biologically functional tissues. This can certainly be achieved through developing composite nanofi bers. This article gives a brief overview on the current development and application status of employing electrospun composite nanofi bers for constructing biomimetic and bioactive tissue scaffolds. Considering that composites consist of at least two material components and phases, this review details three different confi gurations of nanofi brous composite structures by using hybridizing basic binary material systems as example. These are components blended composite nanofi ber, core-shell structured composite nanofi ber, and nanofi brous mingled structure.
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …
01 Jan 2004
TL;DR: Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance and describes numerous important application areas such as image based rendering and digital libraries.
Abstract: From the Publisher: The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.
TL;DR: Zhang et al. as mentioned in this paper proposed a novel neural network architecture, which integrates feature extraction, sequence modeling and transcription into a unified framework, and achieved remarkable performances in both lexicon free and lexicon-based scene text recognition tasks.
Abstract: Image-based sequence recognition has been a long-standing research topic in computer vision. In this paper, we investigate the problem of scene text recognition, which is among the most important and challenging tasks in image-based sequence recognition. A novel neural network architecture, which integrates feature extraction, sequence modeling and transcription into a unified framework, is proposed. Compared with previous systems for scene text recognition, the proposed architecture possesses four distinctive properties: (1) It is end-to-end trainable, in contrast to most of the existing algorithms whose components are separately trained and tuned. (2) It naturally handles sequences in arbitrary lengths, involving no character segmentation or horizontal scale normalization. (3) It is not confined to any predefined lexicon and achieves remarkable performances in both lexicon-free and lexicon-based scene text recognition tasks. (4) It generates an effective yet much smaller model, which is more practical for real-world application scenarios. The experiments on standard benchmarks, including the IIIT-5K, Street View Text and ICDAR datasets, demonstrate the superiority of the proposed algorithm over the prior arts. Moreover, the proposed algorithm performs well in the task of image-based music score recognition, which evidently verifies the generality of it.
TL;DR: The importance of electrospinning for biomedical applications like tissue engineering drug release, wound dressing, enzyme immobilization etc. is highlighted in this paper, where the focus is also on the types of materials that have been electrospun.
Abstract: The electrospinning technique provides non-wovens to the order of few nanometers with large surface areas, ease of functionalisation for various purposes and superior mechanical properties. Also, the possibility of large scale productions combined with the simplicity of the process makes this technique very attractive for many different applications. Biomedical field is one of the important application areas among others utilising the technique of electrospinning like filtration and protective material, electrical and optical applications, sensors, nanofiber reinforced composites etc. Electrospinning assembly can be modified in different ways for combining materials properties with different morphological structures for these applications. The importance of electrospinning, in general, for biomedical applications like tissue engineering drug release, wound dressing, enzyme immobilization etc. is highlighted in this feature article. The focus is also on the types of materials that have been electrospun and the modifications that have been carried out in conventional electrospinning apparatus keeping in view the specific needs for various biomedical applications.
TL;DR: The purpose of this review is to take a closer look on the wound dressing applications of biomaterials based on chitin, chitosan and their derivatives in various forms in detail.
Abstract: Wound dressing is one of the most promising medical applications for chitin and chitosan. The adhesive nature of chitin and chitosan, together with their antifungal and bactericidal character, and their permeability to oxygen, is a very important property associated with the treatment of wounds and burns. Different derivatives of chitin and chitosan have been prepared for this purpose in the form of hydrogels, fibers, membranes, scaffolds and sponges. The purpose of this review is to take a closer look on the wound dressing applications of biomaterials based on chitin, chitosan and their derivatives in various forms in detail.