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swift ocr text: May 20, 2019 · In this tutorial, you'll learn how to use Tesseract, an open-source OCR engine maintained by Google, t ...google ocr api iosasp.net core ocr, ios 11 text recognition, .net ocr open source, online ocr dotnet, azure ocr python, hindi ocr software free download for windows 8, java-ocr-api jar download, microsoft ocr library for windows runtime download, linux free ocr software, windows tiff ocr, php ocr class, vb.net ocr library for windows runtime, handwriting ocr software for mac, azure cognitive services ocr pdf, c++ ocr ios ocr handwriting Tesseract OCR Tutorial for iOS | raywenderlich.com
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In the previous chapters we saw that in the analysis of many operational systems the concepts of a state of a system and a state transition are of basic importance For dynamic systems with a given probabilistic law of motion, the simple Markov model is often appropriate However, in many situations with uncertainty and dynamism, the state transitions can be controlled by taking a sequence of actions The Markov decision model is a versatile and powerful tool for analysing probabilistic sequential decision processes with an in nite planning horizon This model is an outgrowth of the Markov model and dynamic programming The latter concept, being developed by Bellman in the early 1950s, is a computational approach for analysing sequential decision processes with a nite planning horizon The basic ideas of dynamic programming are states, the principle of optimality and functional equations In fact dynamic programming is a recursion procedure for calculating optimal value functions from a functional equation This functional equation re ects the principle of optimality, stating that an optimal policy has the property that whatever the initial state and the initial decision, the remaining decisions must constitute an optimal policy with regard to the state resulting from the rst transition This principle is always valid when the number of states and the number of actions are nite At much the same time as Bellman (1957) popularized dynamic programming, Howard (1960) used basic principles from Markov chain theory and dynamic programming to evelop a policy-iteration algorithm for solving probabilistic sequential decision processes with an in nite planning horizon In the two decades following the pioneering work of Bellman and Howard, the theory of Markov decision processes has expanded at a fast rate and a powerful technology has developed However, in that period relatively little effort was put into applying the quite useful Markov decision model to practical problems. 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