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Asprise Java OCR ( optical character recognition ) and barcode recognition SDK offers a high performance API library for you to equip your Java applications ...

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The argumentation is then identical to that in the proof of Theorem 41; even the time bound T can remain unchanged Polynomial for Unlimited Processors While in general the scheduling problem without communication costs is NP-complete, it is solvable in polynomial time for an unlimited number of processors Note that this is in contrast to the general scheduling problem; see Theorem 42 A simple algorithm to nd an optimal schedule is based on two ideas: 1 Each node is assigned to a distinct processor 2 Each node starts execution as soon as possible Since there is only one task on each processor, the earliest start time is only determined by the precedence constraints of the task graph; processor constraints cannot occur (Condition 41) A node can start its execution as soon as its data is ready From Eq (417) of De nition 416, it is clear that the DRT is not a function of the processors on which the involved nodes are executed This is the essential difference from the general case, where communication costs must be considered Hence, the earliest possible start time of a node is its DRT, and each node should start at that time: ts (n) = tdr (n) n V (418)



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This comparison of optical character recognition software includes: OCR engines​, that do the ... Plain text, searchable PDF, XML, Java, C#, VB.NET ... NET OCR SDK based on Cognitive Technologies' CuneiForm recognition engine.

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Fig. 2.2 Logscale diagrams. Left: An example of the yj against j plot and regression line for a LRD process with strong short-range dependence. The vertical bars at each octave give 95% con dence intervals for the yj . The series is simulated FARIMA (0; d; 2) with d 0:25 and second-order moving average operator C B 1 2B B2, implying a; cf 0:50; 6:38 . Alignment is observed over scales j1 ; j2 4; 10 , and a weighted regression over this range ^ allows an accurate estimation despite the strong short-range dependence: a 0:55 0:07, ^ ^ cf 6:0 with 4:5 < cf < 7:8. The scaling can be identi ed as LRD as the value is in the ^ correct range, a P 0; 1 , and the alignment region includes the largest scales in the data. ^ Right: Alignment is observed over the full range of scales with a 2:57, corresponding to ^ H 0:79, consistent with the self-similarity of the simulated FBM (H 0:8) series analyzed.





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Add image based Optical Character Recognition feature to any Java-based ... Developers can easily read characters in multiple fonts, rotated images, whole ...

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the identi cation of the kind of scaling is made by interpreting the estimated value in the context of the observed range. These different aspects of the aims and use of the logscale diagram are expanded upon next. 2.3.1.2 The Detection of Scaling A priori it is not known over which scales, if any, a scale-invariant property may exist. By the detection of scaling in the logscale diagram we mean the identi cation of region(s) of alignment and the determination of their lower and upper cutoff octaves, j1 and j2, respectively, which are taken to correspond to scaling regimes. In a sense this is an insoluble problem, as scaling often occurs asymptotically or has an asymptotic de nition, with no clear way to de ne how a scaling range begins or ends. Nonetheless experience shows that good estimates are possible. Note the semantic difference between the term scaling region or range, a theoretical concept that refers to where scaling is truly present (an unknown in real data), and alignment region or range, an estimation concept corresponding to what is actually observed in the logscale diagram for a given set of data. The rst essential point here is that the concept of alignment is relative to the con dence intervals for the yj , and not to a close alignment of the yj themselves. Indeed, an undue alignment of the actual estimates yj indicates strong correlations between them, a highly undesirable feature typical of time domain log log based methods such as variance time plots. As mentioned earlier the mj , and hence the yj , are weakly dependent, resulting in a natural and desirable variation around

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Feb 20, 2018 · Optical Character Recognition, or OCR is a technology that enables you ... For this exercise I use a Dockerized Java Spring — boot application ...

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An option if you want to also download the Aspose.OCR for Java API - Example Source Codes (To copy/import later into the project with the help of Aspose.OCR Example wizard). Aspose.OCR Example wizard lets you create / copy or import the downloaded Aspose.OCR for Java API - Example Source Codes into the project.

the calculated regression line as seen, for example, in Fig 22 Using weighted regression incorporates the varying con dence intervals into the estimation phase; however, the selection of the range of scales de ning the alignment region is prior to this, and great care is required to avoid poor decisions We now discuss the selection, in practice, of the cutoff scales j1 and j2 A preliminary comment is that for the regression to be well de ned at least two scales are required, for a Chi-squared goodness of t test three, and in practice four, are needed before any estimate can be taken seriously: it is simply too easy for three points to align fortuitously if the con dence intervals are not very small.

The job of the algorithm is to calculate the DRTs and to assign the start times in the correct order For the calculation of a node s DRT, the nish time of all its predecessors must be known This is easily accomplished by handling the nodes in a topological order (De nition 36), which guarantees that the nish times of all predecessors pred(ni ) of a node ni have already been de ned when ni s DRT is calculated Algorithm 6 shows this procedure in algorithmic form It is assumed that

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Aspose.OCR for Java is a stand-alone OCR API for Java applications while allowing the developers to perform optical character recognition on commonly used ...

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Reading Text from Images Using Java - DZone Java
Mar 10, 2017 · This quick Java app uses the Tesseract library to help turn images into text. ... and simply download the tessdata-master folder from https://github.com/tesseract​-ocr/tessdata .... java ,tesseract ,image-to-text-conversion ,tutorial.












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