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JavaScript optical character recognition demo . Contribute to kdzwinel/ JS - OCR - demo development by creating an account on GitHub.

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ionic-app-base - A base starting point for Ionic, with Cordova, Bower, and Gulp. Javascript. We recommend using the [Ionic ...

visual .net control to generate, create barcode standards 128 . 128 data, size, image with word documents barcode sdk. .In the application of the method of moments to the rough surface scattering problem, a common implementation is to use a grid of 10 points per free-space wavelength o discretize the surface. We shall call such a gridding a single coarse grid (SCG). However, in lossy dielectric surfaces, the wavelength in the dielectric medium is much shorter. Thus in scattering by lossy dielectric rough surfaces with high permittivity, there can be rapid spatial variation of surface fields. For microwave remote sensing applications, both wet soil surfaces and ocean surfaces can have large permittivity. Two alternatives were used. The first alternative is to use impedance boundary condition as shown in 4. The disadvantage of this alternative is that an approximation is used in the problem. The second alternative is to use a dense grid with a large number of points (say more than 30 points) per free-space wavelength. We shall call such a gridding a single dense grid (SDG). We have shown in 4 that dense sampling of points is a requirement for energy conservation and an accurate calculation of emissivity. The disadvantage of this second alternative is that there is a large increase in CPU and required memory. The physics-based two-grid (PBTG) method to be discussed in this section is an improvement over these two alternatives in that it has the same accuracy as the single dense grid and yet has a CPU comparable with that of the single coarse grid. To demonstrate the accuracy of the PBTG method, we use it to calculate the emissivity of a random rough surface. In PBTG, two grids were used: a dense grid and a sparse grid. The.Related: 





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How to extract text from an image using JavaScript - LogRocket Blog
Feb 12, 2019 · ... around precisely what we are interested in, reading text from an image. There is a very promising JavaScript library implementing OCR ...

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javascript OCR API - Stack Overflow
I see this is an old post, but the topic is still open and there are some new players now. You could use the OCR API from HP Haven OnDemand.

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May 29, 2019 · There are many software and libraries available for optical character recognition. After bit of study I chose Tesseract.js library, which is quite ...

) This is the same data used for testing in 12 Hence, a more detailed description of the data can be found in 12 The Mill data set has 68,871 normal use events in 14 normal use sessions and 36,036 attack data in seven attack sessions The Pascal data set has 81,755 normal use events in 63 normal use sessions and 32,327 attack events in four attack sessions Each audit event is considered as an observation of the computer system state at a given time The event type is considered to represent the system state There are 284 possible event types that can be recorded by BSM Hence, there are 284 possible system states However, only 69 different types of audit events appear in the Mill data set, and only 53 different types of audit events appear in the Pascal data set For the Mill data set, the normal events in the last hour of the three-hour Mill data set are used as the training data, and the entire set of the three-hour Mill data is used as the testing data There are 42,983 audit events in the Mill training data with 67 different event types For the Pascal data set, the normal events in the last hour of the three-hour Pascal data set are used as the training data, and the entire set of the three-hour Pascal data is used as the testing data There are 20,616 audit events in the Pascal training data with 53 different event types A Markov chain model, consisting of P and Q in Formulas 151 and 152, respectively, is built from the Mill training data using Formulas 153 and 154 For each audit event in the Mill testing data, this Markov chain model is then used to compute the probability of observing the short state sequence for this event in a window size of T under the normal use condition using Formula 155 Two T values of 10 events and 100 events are tested The ROC method is used to evaluate the detection performance of the Markov chain model-based anomaly detection technique by using a wide range of signal thresholds to obtain pairs of false alarm rate and hit rate For each signal threshold, if the probability of a given event in the Mill testing data is greater than this signal threshold, the event is considered attack; otherwise, the event is considered normal use A false alarm occurs when a normal use event in the testing data is considered attack A hit occurs when an attack event in the testing data is signaled as attack The false alarm rate and the hit rate are computed over all the events in the Mill testing data set Similarly, the Markov chain model-based anomaly detection technique is developed and tested using the Pascal training data and testing data Figure 151 shows the ROC charts of the Markov chain model based anomaly detection technique for the ill data set with the window sizes of 10 events and 100 events Figure 152.

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How to make a simple Optical Character Recognition script ...
26 Apr 2018 ... This post is about making a simple OCR using Microsoft computer vision API.

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