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var Result = Ocr .Read(@"C:\path\to\image.png"); IronOCR is unique in its ability to automatically detect and read text from imperfectly scanned images and PDF documents. ... The OCR ( Optical Character Recognition ) engine views pages formatted with multiple popular fonts, weights ...

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How to use Tesseract OCR 4.0 with C#. Contribute to doxakis/How-to-use-​tesseract-ocr-4.0-with-csharp development by creating an account on GitHub.

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6 Mar 2019 ... Provide robust .NET OCR APIs for accurate and fast text recognition. C# example shows how to extract text from image file using OCR library.

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web service control to generate, create barcode image in centered on an 800 600 pixel gray background Unknown images were taken from the Psychological Image Collection at Stirling (PICS) University database [23], the leix database [24], and the Yale database [22] Mean luminance for all stimuli and off stimulus was approximately equal The rst trial ( Chk ) stimulus was a circular checkerboard display used as a base to verify valid data collection The second trial ( Just Faces ) consisted of a set of 53 images of unknown faces taken from the previously mentioned databases and one image that the subject was familiar with (known) The known image was displayed a total of 53 times and was interspersed randomly with the unknown images Although the unknown images were all unique, they were treated as one overall unknown image and contrasted during analysis with the known image.

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Aug 9, 2017 · Tesseract OCR C# .... Thank you very much, simple and direct, it was very helpful​. .... https ...Duration: 8:01 Posted: Aug 9, 2017

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Mar 6, 2019 · Provide robust .NET OCR APIs for accurate and fast text recognition. C# example shows how to extract text from image file using OCR library.

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The third, fourth, and fth trials ( Noise01, Noise02, and Noise03 ) consist of a set of 54 unknown images and one known image Gaussian white noise with variance = 04, 03, and 005, respectively, was added to each unknown and known image using Matlab s imnoise command An example is provided in Figure 191 The sixth trial ( Mix01 ) consists of a set of four images, both face02, both face03, both face04, and both face05 (see Figure 192) The images are created by replacing vertical and horizontal lines in the known image with vertical and horizontal lines in the unknown image to achieve a pseudo-morphing effect The rst image of the set, both face02bmp, is created by replacing every other vertical and horizontal line in the known image with the corresponding vertical and horizontal lines in the unknown image.

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While happy and screaming faces can be easily distinguish by looking at a small number of pixels (such as the eyes or the corners of the mouth), a pixel-to-pixel comparison may be ecessary to decide whether an image is a neutral expression or a not-excessively-marked angry face In Figure 89b we show the computational times (CT) of Figure 86 multiplied by the percentage (range: 0 1) of pixels that were necessary to use in order to obtain the best classi cation rate when classifying the images as either neutral expressions or the expression under consideration The pixels were selected according to the rankings given by Sb Human Performance QR-Code barcode library for net using asp Stimuli, Design, and Procedure: The neutral, happy, angry, and scream face images of 20 males (with no glasses) of the AR face database were selected or this experiment.

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To prevent recognition by shape alone, images were warped to a standard image size of 165 by 120 pixels Subjects participated in four different tests The rst required them to classify each of the images of the AR database within one of the four categories of that data set Subjects were told in advance of those categories, and an image for each of the expressions was shown to participants before the experiment started The other three tests only involved two types of facial expressions In these two-class experiments, subjects were asked to classify images within these two categories only The two-class experiments comprise the following facial expression images: (a) happy and scream, (b) neutral and angry, and (c) neutral and happy Reaction times (in seconds) and percentage of correct choices were recorded.

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Fifty images were randomly selected and displayed, one at a time, until the subject pressed a key to indicate her/his classi cation choice A 2-s pause (with blank screen) separated each of the images shown to the participants Results: In Figure 810a we show the RT means of all the participants when classifying the images within each of the four groups These results should be compared to the CT predicted by our model and shown in Figure 89b As discussed above, our model predicts that when classifying images into two clearly distinguishable classes, the latter will generally require longer RT because (as demonstrated in Section 831) longer time is required to estimate the DF This was con rmed by our group of subjects Figure 810b We also predicted that when classifying face images within two similar classes, the RT will generally increase.

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Sep 30, 2014 · Demonstrates how to use the Microsoft OCR Library for Windows Runtime to extract text in the specified language from an image.

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14 Oct 2019 ... Scripting API . With the SeeShell scripting API you can access SeeShell's web automation functionality from any programming ... OCR .Space ...












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