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simple ocr c#: Nov 8, 2012 · Simply OCR means Optical Character Recognition. We can extract text and layout information from image f ...



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Mar 7, 2016 · Next Recommended Article Cognitive Services – Optical Character Recognition (​OCR) From An Image Using Computer Vision API And C#.

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Mar 7, 2016 · In this article I am going to show how to do OCR using Tesseract in C#. ... Init(@ "​Z:\NewProject\How to use Tessnet2 library\C#\tessdata", "eng", false);; var result = ocr. ... The following image is the sample OCR image,

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The Dew Review – ABBYY FineReader Engine OCR SDK ...
25 Feb 2013 ... ABBYY FineReader Engine is a powerful OCR SDK to integrate ... sample apps are included with the SDK, including examples in C++, C# , VB.

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Get Text from Image or PDF - OCR in C# Example - Aquaforest
C# code to get text from Image or PDF via OCR in Aquaforest OCR SDK. This code sample is included in the trial download.

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Optical Character Recognition with C# in Classic Desktop ...
19 Mar 2016 ... First open Visual Studio and create a new C# Console application .... is a good open source option for optical character recognition in C#  ...

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Dec 16, 2018 · Tesseract is probably the most accurate open source OCR engine available. Combined with the Leptonica Image Processing Library it can ...

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If you are using Visual Studio 2015 and Windows 10, the ... You can see that Windows.Media.Ocr is available to use. Note: The following works ...

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Web API test app for the OCR.SPACE Free OCR API as Visual Studio C# project. - A9T9/Free-OCR-API-CSharp.

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For the collected data of the Windows performance objects described in 7, the same data screening procedure as described in Section 82 is performed to eliminate the data variables, each of which has the observations of the same value under all three conditions: the inactive, attack and norm conditions Each of the remaining data variables is analyzed to extract the autocorrelation feature and discover the autocorrelation change characteristics of he attacks in the following steps 1 For the 10-minute data under the inactive condition and the attack data for the entire attack period from Run 1 of the data collection: (a) Compute the lag-i autocorrelation coef cient of the inactive data and determine the statistical signi cance of the autocorrelation coef cient, for i = 1, , 10 If the autocorrelation coef cient for every one of the ten lags is statistically signi cant, the autocorrelation of the inactive data is considered high (AH) If the autocorrelation coef cient is not statistically signi cant for any of the ten lags, the autocorrelation of the inactive data is considered low (AL) If there is at least one but not all autocorrelation coef cients for the ten lags are statistically signi cant, the autocorrelation of the inactive data is considered medium (AM) (b) Repeat Step 1a for the attack data to determine if the attack data is considered AL, AM, or AH (c) Compare the autocorrelation levels of the inactive data and the attack data, if the autocorrelation level increases from the inactive condition to the attack condition, mark the autocorrelation increase, A+, as the autocorrelation change characteristic that occurs in this variable under this attack; if the autocorrelation level decreases from the inactive condition to the attack condition, mark the autocorrelation decrease, A , as the autocorrelation change characteristic that occurs in this variable under this attack; 2 Repeat Step 1 but replace the 10-minute inactive data and the attack data from Run 1 with the 10-minute inactive data and 10-minute norm data of text editing from Run 2 of the data collection to identify the autocorrelation change characteristics of the text editing norm 3 Repeat Step 1 but use the 10-minute inactive data and 10-minute norm data of web browsing from Run 3 of the data collection to identify the autocorrelation change characteristics of the web browsing norm For each attack, each autocorrelation change characteristic of the attack is examined to see if the same characteristic (the same variable with the same autocorrelation change) also manifests.

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For the collected data of the Windows performance objects described in 7, the same data screening procedure as described in Section 82 is performed to eliminate the data variables which have the observations of the same value under all three conditions: the inactive, attack and norm conditions Each of the remaining data variables is analyzed to extract the wavelet feature and discover the avelet change characteristics of attack and normal use data For the data sample of a given data variable under each condition (inactive, attack and norm) of the collected data, the wavelet transform is performed using each of the ve wavelet forms The statistical toolbox of MATLAB Version 650180913a (R13) is used to perform the wavelet transforms and obtain the wavelet coef cients For the wavelet transform using the Haar and Daubechies wavelets, the k value of 8 is applied to a data sample of 256 data observations Three frequency bands are de ned with the low frequency band containing the three lowest frequencies, the high frequency band containing the three highest frequencies, and the medium frequency band containing the remaining two frequencies [6] For the Paul, DoG and Morelet wavelet transforms applied to each data variable, there are 29 frequencies for 256 data observations These frequencies are considered to fall into three frequency bands: the low frequency band containing the eight lowest frequencies, the high frequency band containing the twelve highest frequencies, and the medium frequency band containing the remaining nine frequencies [6] For each wavelet transform of each variable under each condition (inactive, attack and norm), the Signal Strength (SS) at each frequency band is computed using the wavelet coef cients at that frequency as follows: SS = 1 n.

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Many objects have periodic behavior and emit special signals at a certain frequency [1, 2] For example, the ARP Poison attack sends the ARP replies with the false MAC address to the victim computer at a given frequency The frequency of signal in data over time has long been used for signal detection or object identi cation due to the fact that many objects have their special timefrequency signal characteristics Fourier analysis has traditionally been used to analyze and represent signal frequencies However, Fourier analysis does not reveal the speci c time location of a given frequency characteristic Wavelet analysis allows the analysis and representation of time-frequency signal in both frequency characteristics and their time locations [3, 4] This chapter describes wavelet analysis to extract the wavelet feature of attack and normal use data By comparing the wavelet feature of data collected under inactive, attack and norm conditions, wavelet change characteristics for the eleven attacks described in 7 are uncovered In this chapter, ve wavelet forms, including the Paul wavelet, the Derivative of Gaussian (DoG) wavelet, the Haar wavelet, the Daubechies wavelet, and the Morlet wavelet [1, 2] which represent the ve data patterns of spike, random uctuation, step change, steady change, and sine-cosine with noise described in 9, respectively, are analyzed to uncover the wavelet change characteristics of attack and normal use data At rst, the wavelet analysis is rst introduced using the example of the Haar wavelet The procedure of analyzing the wavelet signal strength and its change from the inactive condition to the attack condition to uncover the attack data characteristics is then described Finally, the wavelet change attack characteristics re presented.

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Sep 12, 2014 · The implementation is far less straight-forward. Prior to Office 2010, Microsoft Office Document Imaging (MODI) was available for OCR.

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Using the Vision API with C# - Google Codelabs
The Google Cloud Vision API allows developers to easily integrate vision detection features within applications, including image labeling, face and landmark detection, optical character recognition ( OCR ), and tagging of explicit content. In this codelab you will focus on using the Vision API with C# .












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