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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 detection of explicit ... Download the Cloud Vision API v1 Client Library for Java .

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Use the Optical Character Recognition ( OCR ) API from a REST Client
4 Sep 2019 ... Discover how to call the Optical Character Recognition ( OCR ) API from a ... the Get your OAuth Access Token using a REST Client tutorial .

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kba/awesome-ocr: Links to awesome OCR projects - GitHub
Links to awesome OCR projects https://github.com/kba/awesome- ocr ... OCR libraries by programming language. Go; Java .Net; Object Pascal; PHP; Python ...

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java - ocr - api » 15.3.0.3 - Maven Repository
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Tesseract OCR with Java with Examples - GeeksforGeeks
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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Tess4J Tutorial with Maven And Java – Linux Hint
In this lesson on Tesseract with Java and Maven, we will see how we can develop ... Tess4J is simply described as a Java JNA wrapper for Tesseract OCR API .

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Apr 17, 2014 · If you search for Java and OCR you will variously find: Tesseract (http://tess4j.sourceforge.net/). This is a de facto standard, BUT it's C(++) wrapped in Java. That will be a nightmare to redistribute.

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Complexity In comparison to contention aware list scheduling, the time complexity under the involvement contention model does not increase The additional scheduling of the edge on the processors increases the effort to implement the algorithm, but it does not modify its time complexity For example, the complexity of the second part of simple list scheduling (Algorithm 9) with start/ nish time minimization and the end technique is O(P(V + EO(routing))) (see Section 751) Also, the complexity does not increase for the insertion technique (Section 61), even though the individual scheduling of a node and an edge on a processor is more complex In the involvement contention model, there are more objects on the processors, namely, nodes and edges, which must be searched to nd an idle time interval On all processors there are at most O(V + E) nodes and edges; hence, the total complexity for the scheduling of the nodes increases from O(V2 ), under the contention model, to O(V(V + E)) The scheduling of the edges on the processors is O(E(V + E)), compared to O(PE) with the end technique, and on the links it remains O(PE2 O(routing)) Hence, the total complexity of the second part of simple list scheduling with start/ nish time minimization and the insertion technique is O 2 + VE + PE2 O(routing)), that is, O(V2 + PE2 O(routing)), as under the contention model (Section 751) 842 Two-Phase Heuristics The generic form of a two-phase scheduling algorithm was already outlined in Algorithm 13 The rst phase establishes the processor allocation and the second phase constructs the actual schedule with a simple list scheduling heuristic, as described in Section 52 The next paragraphs discuss how such an algorithm is applied under the involvement contention model Phase 1 Processor Allocation The processor allocation can originate from any heuristic or can be extracted from a given schedule For example, a schedule produced under the classic or contention model might serve as the input (see also Section 52) Using the rst two steps of a clustering based heuristic, that is, the clustering itself and the cluster-to-processor mapping (Algorithm 14, Section 53), is very promising, because clustering tries to minimize communication costs, which is even more important under the involvement contention model The clustering itself can be performed under the classic or, as described in Section 753, under the contention model Even the involvement contention model can be used with a straightforward extension of the considerations made in Section 753 Genetic Algorithm In Sinnen [172] and Sinnen et al [180], a genetic algorithm, called GICS (genetic involvement contention scheduling), is proposed for the determination of the processor allocation The simple idea is that the genetic algorithm searches for an ef cient processor allocation, while the actual scheduling is performed with a list scheduling heuristic as discussed later for phase 2.

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J4L OCR tools for the Java [TM] Platform - J4L Components
The J4L OCR tools is set of components that can be used to include OCR capabilities in Java applications. That means you can receive faxes, PDF files or scan ...

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Download free Asprise Java OCR SDK - royalty-free API library with ...
Asprise Java OCR library offers a royalty-free API that converts images (in formats like JPEG, PNG, TIFF, PDF, etc.) into editable document formats Word, XML, ...












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