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High performance, royalty-free Java OCR and barcode recognition on Windows, Linux, Mac OS and Unix. ... You can convert images (in various formats like JPEG, PNG, TIFF, PDF , etc.) into editable document formats (Word, XML, searchable PDF , etc.). ... With enhanced image processing and text ...

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 wavelet 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 sing 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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OCR with Java and Tesseract – Brandsma Blog
7 Dec 2015 ... Introduction. Ever wanted to scan ( OCR ) a document from an application ? You may want to take a look at Tesseract. Tesseract is ocr engine ...

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Jun 18, 2015 · A Java OCR SDK Library API allows you to perform OCR and bar code recognition on images (JPEG, PNG, TIFF, PDF, etc.) and output as plain ...

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OCR with Java and Tesseract – Brandsma Blog
7 Dec 2015 ... You may want to take a look at Tesseract . Tesseract is ocr engine once developed by HP. Currently it is an opensource project sponsored by ...

hand, the ef ciency (fom) is dramatically improved and every calculation with p 09 passes MCNP s ten statistical checks ([23], see also Section 1019) In particular, note that the tail slope estimate (k > 4) is indicating at least three nite moments for all calculations, so that use of the central limit theorem is possible, unlike the calculations in Table 101 The last line shows that even with a very poor choice of a very high transform parameter, the ail slope indicates that the variance is nite Elaborating a bit more, using the central limit theorem requires at least two nite moments Eventually, the high score tail of the density f (x) must decrease faster than 1/x 3 (slope 3), or else the second moment, x 2 f (x) dx, will not be nite Note that it is even better if the high score tail of the density f (x) decreases faster than 1/x 5 (slope 5), so that four nite moments exist When four moments exist, not only is the central limit theorem applicable, but also the sample variance is usually a good estimate of the true variance used in the central limit theorem Valid con dence intervals are thus much more likely when four moments exist A complete print (event log) of the largest scoring history for p = 06 indicates that one of the (relatively rare) high-energy 14 MeV source neutrons is responsible for the largest score At this point, one would attempt to bias the sampling of the source so that more 14 MeV neutrons were sampled, with correspondingly smaller weights One could proceed by using the weight window generator to estimate an energy-dependent weight window, then adjust the source energy bias so that the source particles are within their space-energy weight windows Space does not permit description here, but see [3] for an illustration of source energy bias and space-energy weight windows.

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As I know, Yunmai Technology OCR library may be a good choice for you. Yunmai Technology is also a professional developer of (Optical Character ...

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A Java OCR SDK Library API allows you to perform OCR and bar code recognition ... How much is this artifact used as a dependency in other Maven artifacts in ...

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bar code decoder for net Using Barcode recognizer for The watermark embedding procedure is sketched in Figure 11 It consists of embedding a watermark sequence, which is usually binary, into host data by means of a key In the detection phase, the key is used to verify the presence of the embedded sequence With regard to the domain where the watermark embedding occurs, we can distinguish methods operating in the spatial domain [15], in the discrete cosine transform DCT domain [16 19], in the Fourier transform domain [20], and in the wavelet transform domain [1 5] When unwanted modi cations of the watermarked data affect even the extracted watermark, the embedding scheme is known as fragile Fragile watermarking [6 8] can be used to obtain information about the tampering process.

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In fact, it indicates whether or not the data has been altered and supplies localization information as to where the data was altered Here, an unconventional use of a fragile watermark to evaluate the QoS in multimedia mobile communications is presented Speci cally, a known watermark is superimposed onto the host data The rationale behind this approach is that, by transmitting the watermarked With regard to the domain where the watermark embedding occurs, we can distinguish methods operating in the spatial domain [ 15], in the discrete cosine transform DCT domain 16 19], in the Fourier transform domain [20], and in the wavelet transform domain [1 5] When unwanted modi cations of the watermarked data affect even the extracted watermark, the embedding scheme is known as fragile Fragile watermarking [6 8] can be used to obtain information about the tampering process.

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A naive approach uses a single polynomial approximation P (t) for the whole interval [0, t0 ] A polynomial approximation that is easy to handle is the Chebyshev approximating polynomial Gauss Legendre integration is then recommended to evaluate the required function values of bapp (s) A code to compute the function values of the Chebyshev approximating polynomial at the points used in the numerical integration procedure can be ound in the sourcebook by Press et al (1992) One has a smooth function P (t) when using a single Chebyshev polynomial approximation P (t) for the whole interval [0, t0 ] However, a better accuracy is obtained by a more re ned approach in which the function b(t) on the interval [0, t0 ] is replaced by a piecewise polynomial approximation on each of the subintervals of length with as in (F2) Den Iseger (2002) suggests approximating b(t) on each of the subintervals [k , (k + 1) ) by a linear combination of Legendre polynomials of degrees 0, 1, , 2n 1 with n as in (F2) This leads to an approximating function with discontinuities at the points k However, this dif culty can be resolved by the modi cation (F7) for non-smooth functions Details can be found in Den Iseger (2002) A simpler approach seems possible when the analytically intractable Laplace transform b (s) is given by b (s) = E(e sX ) for a continuous random variable X with a strictly increasing probability distribution function F (x) Then b (s) = E[g(U, s)] for a uniform (0, 1) random variable 1 U , where g(u, s) = exp( sF 1 (u)) The (complex) integral 0 g(u, s) du can be evaluated by Gauss Legendre integration The required numerical values of the inverse function F 1 (u) may be obtained by using bisection.

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Reading Text from Images Using Java - DZone Java
10 Mar 2017 ... This quick Java app uses the Tesseract library to help turn images into text. ... 10, 17 · Java Zone · Tutorial ... skip Step 2 to Step 5 and simply download the tessdata-master folder from https://github.com/tesseract- ocr /tessdata.

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Quickstart: Extract printed text (OCR) using the Computer Vision REST API and Java. 07/02/2019; 3 мин. за читање. Create and run the sample ... · Examine the response












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