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Feb 15, 2019 · Build an AI-powered iOS app that can extract text data from an ... Once Firebase is in place, we can begin with our text detection API integration.

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Apr 29, 2018 · Swift - Optical Character Recognition (OCR) Tutorial ... Check out our iOS Course​: http://www ...Duration: 13:18 Posted: Apr 29, 2018

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Jul 16, 2018 · First, let's set up our project with AVFoundation for camera tracking. .... like solutions that work well with Swift, so I tried SwiftOCR first. It's written ...

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Feb 26, 2019 · Could Not Build Objective-C Module 'RealmSwift'. This bug fix is brought to you by the developer of MoneyPhone: Personal Expense Tracking ...

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If you need to convert the image to text for OCR then you can use the following .... And, thats not enough to get text out from an image with swift.

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logarithmic factors) of the trajectories Indeed, FBM is a good candidate for modeling phenomena which, using a statistical processing, are found to have C H trajectories and are supposed, for theoretical reasons, to be self-similar The importance of the identi cation of H, starting from the samples of the phenomenon necessarily taken in discrete time, is thus crucial At the same time, it is necessary to remember that FBMs are processes with stationary increments, which simpli es the spectral study of the process but is too restrictive for certain applications Indeed, in many elds (when we want to simulate textures in an image), it is expected, a priori, that order H depends on the point at which the process is observed For example, if, using a random eld, we want to model the aerial photographing of a forest, we would like to have a model where the parameter of self-similarity around the point x, noted by h(x), depends on the geological nature of the ground in the vicinity of x However, a spatial modulation of the eld law is generally incompatible with the property of self-similarity, which is the overall property Consequently, the problem consists of arriving at a concept of a suf ciently exible, locally self-similar eld, so that the parameters which de ne the eld law could vary with the position, yet be simple enough to enable the identi cation of these parameters Unfortunately, the simple approach, which consists of reproducing H by a function h(x) in the formula giving the covariance of a FBM, is not satisfactory: generally, we can show that there does not exist any Gaussian eld having this generalized covariance We will thus have to introduce the mathematical tools which will make it possible to build models generalizing FBMs and also to identify the functional parameters of these models These theoretical recaps will be dealt with in section 62, where we will consider each time the relevance of the concept introduced for an example of fractional Brownian motion In this context, we discuss traditional techniques for the study of Gaussian elds and also the tools of analysis in wavelets Using this theoretical framework, in section 63 we will formally de ne the property of local self-similarity and present two examples which form the base of all the later models Having established that these models are not suf ciently general for the applications, we will penetrate into the multifractional world in section 64 In each preceding model, speci c attention will be given to the regularity of the trajectories and, in section 65, we shall develop the statistical methods which make it possible o estimate this regularity This is what we call model identi ability At this point, it is necessary to clarify that the term elds is used for families 1 In applications, the most of random variables indexed by groups of d for d interesting cases correspond to d > 1 (for example, d = 2, for the images) However, certain statements, particularly those concerning identi cation, will relate only to processes (ie, elds where we have d = 1).

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In the rst strategy that we will consider, if the angle between Vi and the direction toward Ti is larger than the maximum turn the robot can make in one tep, the robot will attempt a fast smooth maneuver by turning at the maximum rate until the directions align; hence the name Maximum Turn Strategy Once a step is executed, new sensing data appear, a new point Ti+1 is sought, and the process repeats That is, the actual path and the path that contains points Ti will likely be different paths: With the new sensory data at the next step, the robot may or may not be passing through point Ti In the second strategy, at each step, a canonical solution is found which, if no obstacles are present, would bring the robot from its current position Ci to the intermediate target Ti with zero velocity and in minimum time.

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Hence the name Minimum Time Strategy (The minimum time refers of course to the current local piece of scene) If the canonical path crosses an obstacle and is thus not feasible, a near-canonical solution path is found which is collision-free and satis es the control constraints We will see that in this latter case only a small number of options needs be considered, at least one of which is guaranteed to be collision-free The fact that no information is available beyond the sensing range dictates caution To guarantee safety, the whole stopping path must not only lie inside the sensing range, it must also lie in its visible part No parts of the stopping path can be occluded by obstacles.

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25 Jun 2015 ... I know you said open source , and just wanted to say, I went down that ... You can get Abbyy OCR Cloud SDK and its $0.10 per page (goes down with volume). ..... Really happy with the results for my iOS app (universal app) ...

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how to convert image to text using iOS swift? - Stack Overflow
1) Open Source OCR - Tesseract http://code.google.com/p/ tesseract -ocr/ - completely free, but less accurate. This link will show how to run in ...












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