The apartment complexes near Western Kentucky University sued the United States Postal Service and a postmaster in January after the agency began delivering mail in bulk to property management offices instead of tenants' mailboxes. The change came after the Postal Service reclassified the residences as dormitories, according to the lawsuit.

Matching shapes can be much difficult task then just matching images, for example recognition of hand-written text, or fingerprints. Because most of shapes that we trying to match is heavy augmented. I can bet that you will never write to identical letters for all your life. And look at this from the point of people detection algorithm based on handwriting matching -- it would be just hell. Of course in the age of Neural networks and RNNs it also can be solved in a different way then just straight mathematics, but not always you can use heavy and memory hungry things like NNs.

Applying machine learning techniques to security solutions is one of the current AI trends. This article will cover the approach to developing OCR-based software using deep learning algorithms. This software can be used to analyze and process identification such as a US driver's license as part of a security system for verifying identity. OCR (Optical Character Recognition) technology is already used by machine learning companies for business processes automation and optimization, with use cases ranging from Dropbox using it to parse through pictures to Google Street view identifying different street signs to searching through text messages and translating text in real time. In this particular case, OCR can be used as part of an automated biometric verification system.

Neural network based end-to-end text to speech (TTS) has significantly improved the quality of synthesized speech. Prominent methods (e.g., Tacotron 2) usually first generate mel-spectrogram from text, and then synthesize speech from the mel-spectrogram using vocoder such as WaveNet. Compared with traditional concatenative and statistical parametric approaches, neural network based end-to-end models suffer from slow inference speed, and the synthesized speech is usually not robust (i.e., some words are skipped or repeated) and lack of controllability (voice speed or prosody control). In this work, we propose a novel feed-forward network based on Transformer to generate mel-spectrogram in parallel for TTS. Specifically, we extract attention alignments from an encoder-decoder based teacher model for phoneme duration prediction, which is used by a length regulator to expand the source phoneme sequence to match the length of the target mel-spectrogram sequence for parallel mel-spectrogram generation. Experiments on the LJSpeech dataset show that our parallel model matches autoregressive models in terms of speech quality, nearly eliminates the problem of word skipping and repeating in particularly hard cases, and can adjust voice speed smoothly.

IDCC uniquely leverages optical character recognition, Utopia's advanced machine learning code, intelligent online web search, and document search. Beginning simply with only a photo of a manufacturer's nameplate, IDCC can produce complete and accurate material and asset information. Manufacturer and model data is organized in ISO-14224 standards and can be delivered via a variety of easy-to-integrate methods, including SAP Asset Intelligence Network . The cloud-based nature of IDCC enables cost-effective, rapid deployments by large and small organizations alike. IDCC can be deployed in pure cloud environments, such as SAP Intelligent Asset Management, or hybrid deployments using SAP Master Data Governance, enterprise asset management extension by Utopia.

The dynamic design duo of Karen Ann Donnachie and Andy Simionato are set to receive the Tokyo Type Directors Club award for their AI reading machine project – a machine that essentially transforms books into short Haikus accompanied by related images. It does this by using computer vision and optical character recognition to'read' books. Then with machine learning and natural language processing, it selects a poetic combination of words while erasing the rest to form an artsy-looking Haiku. While doing this, the reading machine also using Google to search up images that relate to said words. Donnachie and Simionato have released a series of books that we know and love with a slight twist.

Developing software that incorporates artificial intelligence (AI) can be unpredictable, and you need a unique set of knowledge and skills to code, test, and make sense of the data. What's more, tuning the system can take time, and the decisions AI-based software makes can sometimes be difficult to explain. My organization specializes in developing software test automation tools that help users develop tests that run on different platforms, such as desktop computers and mobile devices. We wanted to make it even easier to write and run these tests, and avoid having to customize the test for each platform. Our research led to adopt natural-language processing, which allows users of our software to describe a test using simple English, and computer vision with optical character recognition to identify the objects on a screen.

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.

We introduce a large-volume box classification for binary prediction, which maintains a subset of weight vectors, and specifically axis-aligned boxes. Our learning algorithm seeks for a box of large volume that contains simple'' weight vectors which most of are accurate on the training set. Two versions of the learning process are cast as convex optimization problems, and it is shown how to solve them efficiently. The formulation yields a natural PAC-Bayesian performance bound and it is shown to minimize a quantity directly aligned with it. The algorithm outperforms SVM and the recently proposed AROW algorithm on a majority of $30$ NLP datasets and binarized USPS optical character recognition datasets.

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.