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This Pin was discovered by Delphidotnet. Discover (and save!) your own Pins on Pinterest. Data concurrency issues can surface in a number of situations, particularly in distributed, multi-user systems The basic problem with data concurrency is that when working with disconnected data, like you do most of the time through ADONET, the same data can be edited by two different users or pieces of code at the same time When those changes are persisted back to the data store, you have to decide whose changes to keep and what to do about conflicts As described in Appendix D, when a concurrency error occurs in ADONET, a DBConcurrencyException is thrown in the data access layer In large-scale enterprise systems, these errors often will be trapped in the business layer or below it, and handled in some automated fashion, possibly raising some other kind of error to the presentation layer for display to the user In smaller systems, you may allow the DBConcurrencyException to propagate all the way up to the presentation layer for handling there It is also possible that you will choose to have the business layer raise a custom exception in response to a concurrency error Whichever approach you take, there is no "built-in" functionality for resolving these errors or presenting them to a user The sophistication of users varies widely across different application domains, and thus the degree of complexity that you can present to users to resolve such issues varies accordingly, so this precludes a general-purpose solution that would be applicable to everyone If your users are fairly sophisticated, you might present a UI that lets them pick between the values that they are submitting and the values that are currently in the database when concurrency exceptions occur, as shown in Figure 107 ocr sdk free: Microsoft.Windows.Ocr 1.0.0 - NuGet Gallery ocr sdk for mobile Top OCR Platforms to Watch in 2019 | Filestack Blog
1 Feb 2019 ... With that in mind, here are a few of the best OCR platforms and SDKs. Filestack OCR . Filestack OCR was originally a tool built by developers for developers, but has since evolved into including a robust line of products. Tesseract OCR . ABBYY OCR . Anyline OCR . Simple OCR SDK . Smart OCR SDK . Microblink DeepOCR. tesseract-ocr-setup-3.05.01.exe download Comparison of optical character recognition software - Wikipedia
This comparison of optical character recognition software includes: OCR engines , that do the ... Asprise OCR SDK , 1998, 15, 2015, Proprietary, Yes, Yes, Yes, Yes , Yes, Java, ... of the OCR packages Google Docs OCR, Tesseract, ABBYY FineReader , ... for Linux version 1.1.0; ^ " OCR SDK Language Packages Download ". . Data validation is another one of those data-centric features exposed through the properties dialog for all controls that are bound to data (It is also available through the Field or Group Properties dialog for a node in the data source) As with conditional formatting, for data validation you specify one or more conditions that will determine whether the data entered by the user when lling out a form is valid These conditions could be based on such things as preestablished business rules for your department or company Data validation is a step beyond XML Schema data type validation such as simply de ning Whole Number or Date, which we talked about in 3 With data validation, you can de ne a range of allowable whole numbers or restrict the selected date to be within a prescribed how to install tesseract ocr in windows python: https://github.com/Azure-Samples/cognitive-services-python-sdk-samples/ ... (I work at MS in the Azure SDK team, which r ... abbyy ocr sdk Anyline OCR Scanner im App Store
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Asprise Java OCR library offers a royalty - free API that converts images (in ... Java OCR (optical character recognition) and barcode recognition SDK offers a high ... the cubic term In the function I ON3 + N* + 40N + 80, for N = 1000, the value of the function is 10,001,040,080, of which 10,000,000,000 is due to the 1 ON3 term If we were to use only the cubic term to estimate the entire function, an error of about 001 percent would result For sufficiently large N, the value of a function is largely determined by its dominant term (the meaning of szlfJiciently large varies by function) The second reason for measuring the functions' growth rates is that the exact value of the leading constant of the dominant term is not meaningful for different machines (although the relative values of the leading constant for identically growing functions might be) For instance the quality of the compiler could have a large influence on the leading constant The third reason is that small values of N generally are not important For N = 20, Figure 61 shows that all algorithms terminate within 5 ms The difference between the best and worst algorithm is less than a blink of the eye We use Big-Oh notation to capture the most dominant term in a function and to represent the growth rate For instance, the running time of a quadratic algorithm is specified as O(N2) (pronounced "order en-squared") Big-Oh notation also allows us to establish a relative order among functions by comparing dominant terms We discuss Big-Oh notation more formally in Section 64 For small values of N (eg, those less than 40), Figure 61 shows that one curve may be initially better than another, which doesn't hold for larger values of N For example, initially the quadratic curve is better than the O(N log N) curve, but as N gets sufficiently large, the quadratic algorithm loses its advantage For small amounts of input, making comparisons between functions is difficult because leading constants become very significant The function N + 2500 is larger than N 2 when N is less than 50 Eventually, the linear function is always less than the quadratic function Most important, for small input sizes the running times are generally inconsequential, so we need not worry about them For instance, Figure 6 I shows that when N is less than 25, all four algorithms run in less than 10 ms Consequently, when input sizes are very small, a good rule of thumb is to use the simplest algorithm Figure 62 clearly demonstrates the differences between the various curves for large input sizes A linear algorithm solves a problem of size 10,000 in a small fraction of a second The O(N log N) algorithm uses roughly 10 times as much time Note that the actual time differences depend on the constants involved and thus might be more or less Depending on. yunmai technology ocr library Download OCR Software - SimpleOCR
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