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Cloud Vision API Client Library for Java | Google Developers
Cloud Vision API : Integrates Google Vision features, including image labeling, face, logo, and landmark detection, optical character recognition ( OCR ), and ...

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This class describes the usage of TextApp.java. ... google. endpoints. examples. bookstore. BookstoreClient.java. server. src. main. java. com. google ..... Collectors; /** * A sample application that uses the Vision API to OCR text in an image.

443 Granularity For a task graph, the notion of granularity (Section 241) usually describes the relation between the computation and the communication costs Often, the granularity of a task graph is de ned as the relation between the minimal node weight and the maximal edge weight De nition 420 (Task Graph Granularity) Let G = (V, E, w, c) be a task graph G s granularity is minn V w(n) (445) g(G) = maxe E c(e) With this de nition, a task graph is said to be coarse grained if g(G) 1 Coarse granularity is a desirable property of a task graph One objective of task scheduling is always to minimize the cost of communication This is achieved by having as much local communication (ie, communication between tasks on the same processor) as possible Unfortunately, this objective con icts with the other objective of scheduling, namely, the distribution of the tasks among the processors Graphs with coarse granularity can be parallelized (ie, scheduled) more ef ciently, since the in icted cost of communication through parallelization is reasonable to small relative to the cost of computation It is therefore not surprising that certain theoretical results of task scheduling can be established for coarse grained task graphs For example, bounds on the schedule length in relation to the optimal schedule length can be established for coarse grained graphs (Darte et al [52], Gerasoulis and Yang [77], Hanen and Munier [86]) See also Theorem 46 and Theorem 53 of Section 532 Some of the algorithms that will be presented later in this text essentially try to improve (ie, increase) the granularity of a task graph, for example, clustering (Section 53) and grain packing (Section 535) Communication costs are eliminated by grouping nodes together and thereby making communication local, thus cost free Weak Granularity A local ratio between the computation and communication costs is the task or node grain (Gerasoulis and Yang [77]) This ratio measures the communication received and sent by a node in relation to the computation of its predecessors and successors, respectively De nition 421 (Grain) Let G = (V, E, w, c) be a task graph The grain of node ni V is grain(ni ) = min minnj pred(ni ) w(nj ) minnj succ(ni ) w(nj ) , maxeji E,nj pred(ni ) c(eji ) maxeij E,nj succ(ni ) c(eij ) (446).



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Java Code Examples net.sourceforge.tess4j. Tesseract
Project: hadoop-video-ocr File: HadoopOCR. java View source code, 10 votes, vote down vote up ... setDatapath("/usr/share/ tesseract - ocr "); instance.

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routers could allocate resources for a ow to improve routing and forwarding performance For example, a router could cache next-hop routes for common destination addresses to reduce the number of routing computations and to forward related packets along a single route The router could even establish dedicated connections through the switching fabric for long-lived ows to avoid software processing of subsequent packets Each of these network mechanisms performs operations and allocates resources on the time scale of connection arrivals Therefore, the burstiness of the connection arrival process affects two separate provisioning tasks: the central processing unit (CPU) resources necessary to perform the algorithm and the network resources required to acheive a desired level of blocking The burstier the arrival process, the more CPU resources are necessary to execute the algorithm and the more network resources are needed to maintain a given level of blocking.





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Asprise OCR SDK v4.0 – Java Developer's Guide ..... import com.asprise . util . ocr . OCR .... One is aspriseOCR. jar , which is like any other java library; you can just ... First, download the Asprise PDF library from http://asprise.com/product/javapdf.

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Optical Character Recognition ( OCR ) With TESS4J - DZone Web Dev
1 Oct 2015 ... Here's how to implement optical character recognition for images and ... Tess4j is a JNA-based wrapper for Tesseract OCR DLL, the library ... Step 1 :Download the Maven project from here ... .net , java ,web dev ,tess4j. Like (9).

For small arrays of two or three elements, copying using Java code is faster. For arrays larger than this, using arraycopy() is much faster. So,

In this chapter we demonstrate that the TCP connection arrival process is bursty We show that the arrival process is asymptotic self-similar Self-similarity of the TCP connection arrival process implies that the use of standard models in evaluating the performance of resource allocation methods can yield misleading results Therefore, we characterize TCP connection interarrival times1 using heavy-tailed distributions We present statistical evidence that such distributions, especially the Weibull distributions, yield a better model for the interarrival times of TCP connections than exponential models Intuitively, a heavy-tailed interarrival time means that if no connection arrived for some time it becomes more and more unlikely that one will arrive soon This holds even if the underlying arrival process is nonstationary Finally, based on a simple resource allocation problem, we show that there are advantages to using Weibull distributions to model TCP connection interarrival times over a nonstationary Poisson process.

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In this lesson on Tesseract with Java and Maven, we will see how we can ... which accepts a PDF file and returns the text it contains with Tesseract OCR service.

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esseract is an open source framework written in C++ which allows us to work with ... To work with this lesson, it is important to install Tesseract OCR Engine on ...

Our results are based on extensive analyses of multiple traces collected at Carnegie Mellon University in 1995, at AT&T Bell Laboratories in 1995 and 1996, and at AT&T Labs Research in 1996 In addition we augment our results with the analysis of traces collected at Lawrence Berkeley Laboratories (LBL) in 1993 and 1995, at Digital Equipment Corporation (DEC) in 1995, and within AT&T WorldNet in 1997 and 1998 The rest of the chapter is organized as follows In Section 152 we present, on an intuitive level, why TCP connection arrivals are bursty and what impact this may have on resource allocation problems A more detailed description of the TCP=IP traces on which we base our traf c characterizations is given in Section 153 The results in Section 154 indicate the self-similar nature of the arrival process Section 15.

If pred(ni ) succ(ni ) = , that is, the node is independent, the grain is not de ned As an example for this de nition consider Figure 47, where node n has grain(n) = min min{2, 3, 4} min{5, 6} , max{1, 1, 1} max{2, 2} = min 2 5 , 1 2 = 2

5 outlines the methods used to analyze the interarrival time distribution and then shows that the Weibull distribution yields a good t for connection interarrival times Section 156 contrasts the use of the Weibull model to that of a nonstationary Poisson process using an example application Finally we conclude with a brief summary..

1 Given two consecutive timestamps of observed TCP connections from a TCP connection arrival process, the interarrival time is the time difference between the two timestamps.

15.2 BURSTINESS OF TCP CONNECTION ARRIVAL PROCESSES AND ITS IMPLICATIONS In this section we discuss, on an intuitive level, the self-similarity of arrival processes and the reason why TCP connection arrival processes are self-similar. The fact that TCP connection arrivals are bursty leads to the question of how to characterize the distribution of time between arrivals of TCP connections, or the TCP connection interarrival time distribution. Finally, we discuss the implications of the burstiness on resource allocation problems. 15.2.1 Self-Similarity of Packet Arrival Processes

Figure 47 A task graph, where node n is featured as an example for De nition 421 of the grain; node n has grain(n) = min {min{2, 3, 4}/max{1, 1, 1}, min{5, 6}/max{2, 2}} = 2

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In this article, we will learn how to work with Tesseract OCR in Java using the ... Pre-process image data, for example: convert to gray scale, smooth, de-skew, ...

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