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Use Adobe Acrobat DC and learn how to convert PDF to text with optical character recognition ( OCR ) software. Start free trial and easily convert scanned ... tesseract ocr html5 Traditional Zone OCR vs. Dynamic OCR - SimpleIndex - Document ...
Zone OCR is used to read document indexes or tags from text on the page. It is a great way to automate the data entry associated with scanning documents. 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. javascript ocr image: Extract data off a card via a picture - APIs - Bubble Forum ocr library javascript Ocrad.js - Optical Character Recognition in Javascript - Kevin Kwok
Ocrad.js is a pure-javascript version of Antonio Diaz Diaz's Ocrad project, automatically converted using Emscripten. It is a simple OCR ( Optical Character ... jquery ocr libraryHTML5/JavaScript Framework - PDF, OCR, Annotation, Document ...
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Recommendations for barcode creation to be successfully read further: Create 1D barcodes .Related: results in an overall time of four seconds, whereas the fat list_thermometers operation requires one second to execute In other words, the performance difference between the two approaches shrinks from a factor of 16 to a factor of 4 Fat collection manager operations do not scale to large numbers of items For 10,000 devices, list_thermometers returns around 25 MB of data This number is already at or beyond the limit of the maximum call size for many general-purpose ORBs If instead we have 100,000 devices, list_thermometers must return 25 MB of data, which is likely to exceed the memory limitations of either client or server on many systems You can easily get around these problems by adding iterator interfaces to limit the size of the data returned by each call However, iterators make your design and implementation more complex In addition, for a robust system, you must deal with garbage collection of iterators (see 12) The fat operation technique uses structures instead of interfaces that encapsulate state As a result, it is more difficult to modify the system later so that it remains backwardcompatible You cannot create a new version by deriving new interfaces from the old ones In general, the fat operation technique works best if you have large numbers of objects that hold a small amount of state This is because the fat operation technique works best if the overall cost is dominated by call latency As soon as individual objects hold more than a few hundred bytes of state, the overall cost is likely to be dominated by the marshaling rate, and the fat operation technique suffers from diminishing returns The most serious drawback of the fat operation technique is more subtle If you look again at the IDL on page 999, you will notice that the list_thermometers operation can handle only thermometers, whereas the list operation in the original CCS design is polymorphic that is, it can return a list containing both thermometers and thermostats In other words, the fat operation technique loses polymorphism ecause IDL provides only polymorphic interfaces and not polymorphic values[1]. 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