8597703
Evaluation and Application of Recognition Confidence in OCR
Recognition confidence plays an important role in the selection of rejection threshold and the combination of multiple classifiers. In this paper, we first present a systematic theory on classifier's confidence, which includes the definition, the concept of generalized confidence, optimal rejection theorem and the relationship between confidence value and recognition rate. Then we propose a method for the evaluation of recognition confidence. The theory and method are strongly supported by the practice in handwritten numeral recognition and off-line handwritten Chinese character recognition.
2250003
ICDAR 2003 robust reading competitions: entries, results, and future directions
Abstract.This paper describes the robust reading competitions for ICDAR 2003. With the rapid growth in research over the last few years on recognizing text in natural scenes, there is an urgent need to establish some common benchmark datasets and gain a clear understanding of the current state of the art. We use the term ‘robust reading’ to refer to text images that are beyond the capabilities of current commercial OCR packages. We chose to break down the robust reading problem into three subproblems and run competitions for each stage, and also a competition for the best overall system. The subproblems we chose were text locating, character recognition and word recognition. By breaking down the problem in this way, we hoped to gain a better understanding of the state of the art in each of the subproblems. Furthermore, our methodology involved storing detailed results of applying each algorithm to each image in the datasets, allowing researchers to study in depth the strengths and weaknesses of each algorithm. The text-locating contest was the only one to have any entries. We give a brief description of each entry and present the results of this contest, showing cases where the leading entries succeed and fail. We also describe an algorithm for combining the outputs of the individual text locators and show how the combination scheme improves on any of the individual systems.
15560852
Performance analysis of pattern classifier combination by plurality voting
Plurality voting is widely used in pattern recognition. However, there is little theoretical analysis of plurality voting. In this paper, we attempt to explore the rationale behind plurality voting. The recognition/error/rejection rates of plurality voting are compared with those of majority voting under different conditions. It is demonstrated that plurality voting is more efficient in achieving the tradeoff between rejection rate and error rate. We also discuss some practical problems when applying plurality voting to real-world applications.
26474180
Adaptive confidence transform based classifier combination for Chinese character recognition
Abstract Classifier combination is an effective way to improve recognition performance. However, in Chinese character recognition the extremely large number of categories results in several difficulties for the combination. In order to overcome these difficulties a novel combination method is presented in this paper. It consists of three main components: adaptive confidence transform (ACT), consensus theoretic combination and reliability based speedup scheme. ACT, which can estimate a posteriori probabilities from raw measurement values, is the focus of this paper. Experimental results show a significant reduction of error rates in both printed (PCCR) and handwritten Chinese character recognition (HCCR).
8724659
Estimating device availability in pervasive peer-to-peer environment
In pervasive computing environments, devices often communicate in a peer-to-peer manner, either for passing messages or collaboratively running applications. For any particular device, information about the availability of other devices can lead to more efficient communication and better execution of distributed tasks. For example, a device can distribute computation tasks to reliable devices and temporarily avoid unreliable ones. Since there usually is not a central server to coordinate or monitor the communication in such environment, a key technical challenge is for each individual device to predict the availability of other devices. In this paper, we describe some methods for predicting the availability of other devices using historic availability data of those devices obtained in routine usage. In this scheme, each device separately maintains data about the past communication with the other devices, and predicts current and future availability using the statistics of those data. These methods do not require any centralized monitoring or extra probing, and thus have very low computation cost. These characteristics make them suitable for small devices in a peer-to-peer environment.
8341476
Active document layout synthesis
Document layout analysis has been researched for many years. However, there is little work on the reverse of document layout analysis: document layout synthesis, whose goal is to generate logically correct and aesthetically appealing layout given the text/image contents and the flexible layout template. This paper introduces a new automatic document layout synthesis method, which can actively pursue the optimal text block height-width tradeoff simultaneously with the block position adjustment. The key idea is to use a cluster of linear functions to approximate the nonlinear height-width curve. Then two-pass simplex algorithm is used to solve the layout synthesis problem. This method has a wide range of applications, such as variable data printing (VDP), automatic table formatting, and automating document layout ground truth generation.
17019459
Semantic video classification by integrating flexible mixture model with adaptive EM algorithm
Digital video now plays an important role in medical education and healthcare, but our ability to automatic video indexing at semantic level is currently primitive. In this paper, we propose a novel framework to enable more effective semantic video classification and indexing in a specific surgery education video domain. Specifically, this framework includes: (a) A novel semantic-sensitive video content characterization and representation framework by using principal video shots and their perceptual multimodal features. (b) A novel semantic medical concept interpretation technique by using flexible mixture model. (c) A semantic video classifier by using an adaptive Expectation-Maximization (EM) algorithm for automatic parameter estimation and model selection (i.e., selecting the optimal number of mixture Gaussian components). Since more effective video content characterization framework has been integrated with an adaptive EM algorithm for video classification, our semantic video classifier has improved the classification accuracy significantly. For skin classification, its accuracy is close to 95.5%. For semantic surgical video classification, it achieves overall ≈ 84.6% accuracy.
14356230
Delay Optimizations in Quorum Consensus
The management of replicated data in distributed database systems is a classic problem with great practical importance. Quorum consensus is one of the popular methods, combining with eager replication, for managing replicated data. In this paper, we will investigate the problems of delay-optimal quorum consensus. Firstly, we will show that the problem of minimizing the total-delay (or mean-delay) is NP-hard. However, we can show that the problem restricted to some specific network topology, such as trees, rings, and meshes, can be solved in polynomial time. We also developed an approximate algorithm for a general case. The algorithm gives an approximate ratio less than 2. Secondly, we will present an efficient algorithm, based on the dynamic programming technique, to solve the problem of minimizing the maximal-delay.
18316669
On Linear-Spline Based Histograms
Approximation is a very effective paradigm to speed up query processing in large databases. One popular approximation mechanism is data size reduction. There are three reduction techniques: sampling, histograms, and wavelets. Histogram techniques are supported by many commercial database systems, and have been shown very effective for approximately processing aggregation queries. In this paper, we will investigate the optimal models for building histograms based on linear spline techniques. We will firstly propose several novel models. Secondly, we will present efficient algorithms to achieve these proposed optimal models. Our experiment results showed that our new techniques can greatly improve the approximation accuracy comparing to the existing techniques.
8765334
Privacy preserving regression modelling via distributed computation
Reluctance of data owners to share their possibly confidential or proprietary data with others who own related databases is a serious impediment to conducting a mutually beneficial data mining analysis. We address the case of vertically partitioned data -- multiple data owners/agencies each possess a few attributes of every data record. We focus on the case of the agencies wanting to conduct a linear regression analysis with complete records without disclosing values of their own attributes. This paper describes an algorithm that enables such agencies to compute the exact regression coefficients of the global regression equation and also perform some basic goodness-of-fit diagnostics while protecting the confidentiality of their data. In more general settings beyond the privacy scenario, this algorithm can also be viewed as method for the distributed computation for regression analyses.
28636420
Efficient computation of a proximity matching in spatial databases
Abstract Spatial data mining recently emerges from a number of real applications, such as real-estate marketing, urban planning, weather forecasting, medical image analysis, road traffic accident analysis, etc. It demands for efficient solutions for many new, expensive, and complicated problems. In this paper, we investigate a proximity matching problem among clusters and features. The investigation involves proximity relationship measurement between clusters and features. We measure proximity in an average fashion to address possible non-uniform data distribution in a cluster. An efficient algorithm is proposed and evaluated to solve the problem. The algorithm applies a standard multi-step paradigm in combining with novel lower and upper proximity bounds. The algorithm is implemented in several different modes. Our experiment results not only give a comparison among them but also illustrate the efficiency of the algorithm.