Analytic shrinkage is a statistical technique that offers a fast alternative to cross-validation for the regularization of covariance matrices and has appealing consistency properties. We show that the proof of consistency implies bounds on the growth rates of eigenvalues and their dispersion, which are often violated in data. We prove consistency under assumptions which do not restrict the covariance structure and therefore better match real world data. In addition, we propose an extension of analytic shrinkage --orthogonal complement shrinkage-- which adapts to the covariance structure. Finally we demonstrate the superior performance of our novel approach on data from the domains of finance, spoken letter and optical character recognition, and neuroscience.

Book digitizing is an important work in preserving ancient heritages. However, digitizing books contains a series of labor-intensive works, and one of them is to verify optical character recognition (OCR) outcomes. In this paper, we propose a crowdsourceable OCR verification method. Using our method, content holders are able to leverage the power of crowds to complete verification tasks and avoid content leakage. From the experiment results, our method is more efficient and reliable than the traditional method.

There is no commercial character recognition software that supports Thai handwriting. Thai handwritten character recognition is needed to convert handwritten text written on mobile and tablet devices into computer encoded text. We propose a novel method that joins three curve signatures. The first signature is the normalized tangent angle function (TAF), which provides rough classification. The other two novel curve signatures are the relative position matrix (RPM), which is used to compare global curve features, and the straightened tangent angle function (STAF), which is used to compare the tangent angle along the cumulative unsigned curvature domain. In the recognition process, an input curve is extracted for these three signatures and the similarity against each character in the handwriting templates is measured. Then, the similarity scores are weighted and summed for ranking. Our experiment is done on 48 handwriting sample sets (44 Thai consonants appear in each set, and there are 4 sets per handwriting). Our methods yield an accuracy of 94.08% for personal handwriting, and 92.23% for general handwriting.

Crowdsourcing systems, in which numerous tasks are electronically distributed to numerous "information piece-workers", have emerged as an effective paradigm for human-powered solving of large scale problems in domains such as image classification, data entry, optical character recognition, recommendation, and proofreading. Because these low-paid workers can be unreliable, nearly all such systems must devise schemes to increase confidence in their answers, typically by assigning each task multiple times and combining the answers in an appropriate manner, e.g. majority voting. In this paper, we consider a general model of such crowdsourcing tasks and pose the problem of minimizing the total price (i.e., number of task assignments) that must be paid to achieve a target overall reliability. We give a new algorithm for deciding which tasks to assign to which workers and for inferring correct answers from the workers' answers. We show that our algorithm, inspired by belief propagation and low-rank matrix approximation, significantly outperforms majority voting and, in fact, is optimal through comparison to an oracle that knows the reliability of every worker. Further, we compare our approach with a more general class of algorithms which can dynamically assign tasks. By adaptively deciding which questions to ask to the next arriving worker, one might hope to reduce uncertainty more efficiently. We show that, perhaps surprisingly, the minimum price necessary to achieve a target reliability scales in the same manner under both adaptive and non-adaptive scenarios. Hence, our non-adaptive approach is order-optimal under both scenarios. This strongly relies on the fact that workers are fleeting and can not be exploited. Therefore, architecturally, our results suggest that building a reliable worker-reputation system is essential to fully harnessing the potential of adaptive designs.

This paper assumes the hypothesis that human learning is perception based, and consequently, the learning process and perceptions should not be represented and investigated independently or modeled in different simulation spaces. In order to keep the analogy between the artificial and human learning, the former is assumed here as being based on the artificial perception. Hence, instead of choosing to apply or develop a Computational Theory of (human) Perceptions, we choose to mirror the human perceptions in a numeric (computational) space as artificial perceptions and to analyze the interdependence between artificial learning and artificial perception in the same numeric space, using one of the simplest tools of Artificial Intelligence and Soft Computing, namely the perceptrons. As practical applications, we choose to work around two examples: Optical Character Recognition and Iris Recognition. In both cases a simple Turing test shows that artificial perceptions of the difference between two characters and between two irides are fuzzy, whereas the corresponding human perceptions are, in fact, crisp.

In sequence labeling, using higher order features leads to high inference complexity. A lot of studies have been conducted to address this problem. In this paper, we propose a new exact decoding algorithm under the assumption that weights of all higher order features are non-negative. In the worst case, the time complexity of our algorithm is quadratic on the number of higher order features. Comparing with existing algorithms, our method is more efficient and easier to implement. We evaluate our method on two sequence labeling tasks: Optical Character Recognition and Chinese part-of-speech tagging. Our experimental results demonstrate that adding higher order features significantly improves the performance while requiring only 30% additional inference time.

In this paper, we tackle the problem of online semi-supervised learning (SSL). When data arrive in a stream, the dual problems of computation and data storage arise for any SSL method. We propose a fast approximate online SSL algorithm that solves for the harmonic solution on an approximate graph. We show, both empirically and theoretically, that good behavior can be achieved by collapsing nearby points into a set of local "representative points" that minimize distortion. Moreover, we regularize the harmonic solution to achieve better stability properties. We apply our algorithm to face recognition and optical character recognition applications to show that we can take advantage of the manifold structure to outperform the previous methods. Unlike previous heuristic approaches, we show that our method yields provable performance bounds.

Character segmentation is one of the most important decision processes for optical character recognition (OCR). Isolating individual alphabetic characters in the script image is often significant enough to make a decisive contribution towards the success rate of the overall system. An OCR system may be designed to work for either of online and off-line purposes. Online OCR systems collect input data by recording the order of strokes made by the write on an electronic bit-pad, and off-line OCR systems do the same by recording the pixel by pixel digital image of the entire writing with a digital scanner. OCR has a wide field of application covering handwritten document transcription, automatic mail address recognition, machine processing of bankchecks, faxes etc. Off-line OCR of hand written words has long been an active area research. Some important contributions so far made in this field involve analysis of English texts [1], [2], [3], [5], Chinese script [6] and Arabic characters [9]. With this background of research, the present work considers Bengali script for developing suitable techniques for off-line OCR with it.

A resistive memory network that has no crossover wiring is proposed to overcome the hardware limitations to size and functional complexity that is associated with conventional analog neural networks. The proposed memory network is based on simple network cells that are arranged in a hierarchical modular architecture. Cognitive functionality of this network is demonstrated by an example of character recognition. The network is trained by an evolutionary process to completely recognize characters deformed by random noise, rotation, scaling, and shifting. Introduction: Analog neural network hardware has many advantages over its digital and software counterparts.

Crowdsourcing systems, in which tasks are electronically distributed to numerous ``information piece-workers'', have emerged as an effective paradigm for human-powered solving of large scale problems in domains such as image classification, data entry, optical character recognition, recommendation, and proofreading. Because these low-paid workers can be unreliable, nearly all crowdsourcers must devise schemes to increase confidence in their answers, typically by assigning each task multiple times and combining the answers in some way such as majority voting. In this paper, we consider a general model of such rowdsourcing tasks, and pose the problem of minimizing the total price (i.e., number of task assignments) that must be paid to achieve a target overall reliability. We give new algorithms for deciding which tasks to assign to which workers and for inferring correct answers from the workers’ answers. We show that our algorithm significantly outperforms majority voting and, in fact, are asymptotically optimal through comparison to an oracle that knows the reliability of every worker.