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It is inevitable to capture a portion of iris images with motion blur during iris recognition. The texture details on iris patterns are lost in motion blurred images so it may cause recognition performance degradation. This paper presents a first systematic study on the issue of motion blurred iris image recognition. Firstly, the reason of generating motion blurred iris images is analyzed. Secondly,...
This paper describes the installation of a mathematical formula recognition module into an open source OCR system: OCRopus. In particular we consider the identification of inline formulas utilizing existing modules. Text lines including math formulas are first processed using a N-gram language model to reduce the number of formula candidates by thresholding the conditional probability of words. Then...
In a scene image, some characters are difficult to recognize and some others are recognized easily. Such difficult characters usually make the processing time long while easy characters are recognized in a short time. In this paper, we propose a system which recognizes each character with a proper cost for the difficulty. Through the process, easy characters are recognized early and difficult ones...
This paper presents a system for open-vocabulary text recognition in images of natural scenes. First, we describe a novel technique for text segmentation that models smooth color changes across images. We combine this with a recognition component based on a conditional random field with histogram of oriented gradients descriptors and incorporate language information from a lexicon to improve recognition...
Recognizing text in images taken in the wild is a challenging problem that has received great attention in recent years. Previous methods addressed this problem by first detecting individual characters, and then forming them into words. Such approaches often suffer from weak character detections, due to large intra-class variations, even more so than characters from scanned documents. We take a different...
This paper presents an appearance-based scheme for recognition of characters in natural images. In our method, we combine a local subspace classifier (LSC) and transformation-invariance with tangent vectors. In addition, we use negative images of original ones as new training samples for achieving high accuracy. Experimental results on Chars74K and ICDAR03-CH datasets show that the performance of...
While modern off-the-shelf OCR engines show particularly high accuracy on scanned text, text detection and recognition in natural images still remains a challenging problem. Here, we demonstrate that OCR engines can still perform well on this harder task as long as appropriate image binarization is applied to input photographs. For such binarization, we systematically evaluate the performance of 12...
We utilize Coates' unsupervised feature learning method and AdaBoost to detect and recognize part label regions in patent drawings. Image patches are harvested from training data, and features are learned from patterns in image patches. Angle distances between samples and feature banks are computed, and used in AdaBoost classifier. We extract image patches with different sizes to counter the scale...
A novel method using "color drop-out" for document images with "color shift" is proposed. Color shift phenomena sometimes occur in document images captured by a camera device or stand type scanner. It adversely affects the binarization and character recognition processes, because it generates pseudo color pixels on scanned image, which do not exist on the original document. To...
The non-negative matrix factorization (NMF) is a part-Based image representation method which allows only additive combinations of non-negative basis components. NMF has been widely used as a dimensionality reduction technique to solve problems in computer vision and pattern recognition fields. The sparse representation and spatial information of image are also important, however, existing NMF methods...
The recognition of vehicle manufacturer logo is a crucial and very challenging problem, which is still an area with few published effective methods. This paper proposes a new fast and reliable system for Vehicle Logo Recognition (VLR) based on Bag-of-Words (BoW). In our system, vehicle logo images are represented as histograms of visual words and classified by SVM in three steps: firstly, extract...
Contactless palmprint recognition is an effective way to improve the user-friendliness of palmprint recognition technology. The main challenge of contactless palmprint recognition is the intra-class variations caused by contact-less image acquisition. In such occasions, traditional palm-print recognition algorithms which require precise image alignment may not contribute. Aiming at solving this problem,...
This article is focused on automatic recognition of jewelery stones quality. An image recognition method is described. Relevant image characteristics are computed, which are then used to classify the stone quality. Classification is performed by an algorithm based on binary decision trees with the decision thresholds adapted from a training dataset. At the end, the time complexity as well as accuracy...
Traffic Sign Recognition (TSR) is now relatively well-handled by several approaches. However, traffic signs are often completed by one (or several) supplementary sign(s) placed below. They are essential for correct interpretation of main sign, as they specify its applicability scope. The main difficulty of supplementary sub-sign recognition is the potentially infinite number of classes, as nearly...
Object recognition in real scenes is a central problem in computer vision. In this paper we propose a new approach for shape based recognition of objects in real scenes. This approach uses moment invariants for identification of shape features. Moment Invariants are functions of central moments. They are invariant against linear transformations such as rotation, translation and scaling. Therefore,...
This paper presents a new face gender recognition scheme by enjoying the benefit from the dot diffusion among weak classifiers in recognition phase for a low resolution and non-aligned thumbnail image. The main problem of the former Adaboost approaches is that each weak classifier simply offers a binary decision, which fails to compensate the decision error by diffusing it to the rest weak classifiers...
In this paper, a novel implicit video multi-emotion tagging method is proposed, which considers the relations between the users' outer facial expressions and inner emotions as well as the relations among multiple expressions. First, the audiences' expressions are inferred through a multi-expression recognition model, which consists of an image driven expression measurement recognition and a Bayesian...
An automated system capable of recognizing responses for questionnaires and entering them into the database will be very useful in many subjects. Entering data manually is time consuming. Thus, the purpose of the research is to automate the manual data entry process. Through this research, a new clustering method to cluster printed and handwritten words, and character recognition method to identify...
It is important in biometric person recognition systems to protect personal data and privacy of users. This paper introduces a new mechanism to revoke and protect fingerprint minutiae information, which can be used in today's security-aware society. The recently developed minutiae cylinder code (MCC), which provides rotation and translation invariant descriptors for accurate fingerprint recognition...
In a Multimodal biometric system, the effective fusion method is necessary for combining information from various single modality systems. Two biometric characteristics are considered in this study: iris and fingerprint. Multimodal biometric system needs an effective fusion scheme to combine biometric characteristics derived from one or more modalities. The score level fusion is used to combine the...
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