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In this article, we will learn how to work with Tesseract OCR in Java using the Tesseract API . What is Tesseract OCR ? Tesseract OCR is an optical character ... . Studio 2005/2008/2010, Visual Web Developer or Microsoft . ASP.NET Mobile technology support. 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Create Barcode In .NET Framework Using Barcode generator for VS .NET Control to generate, create bar code .Related: QR Code Generating .NET Data, .NET Data Matrix Generating , Generate PDF417 .NET evidence that costs are not equal and incorporating costs into classi cation systems can lead to superior results Our smart data approach to Type I and Type II errors comes with methodology and algorithms that support intended uses Our approach is interoperable and supports quali ed users with diverse needs in an ubiquitous environment It is the product of advanced data engineering technologies including modeling and metadata management and smart application of known standards Bankruptcy prediction is an active area of research in nance Several analytical approaches have been proposed beginning in the late 1960s Among the popular approaches for bankruptcy prediction are the use of statistical discriminant analysis, arti cial neural networks, decision trees, genetic algorithms, and probabilistic approaches, such as logit and probit Researchers use analytical techniques for prediction of bankruptcy Using statistical discriminant analysis and data on a set of companies that went bankrupt during the period 1946 1965, Altman [19] showed that statistical discriminant analysis is a viable tool for prediction of bankruptcy Following Altman s study [19], researchers investigated the use of probabilistic approaches to predict bankruptcy and some used a maximum likelihood estimation of the conditional logit model with the objective of making probabilistic estimates of insolvency Logit is a linear technique that does not require any assumptions about the prior probabilities of bankruptcy or the distribution of predictor variables Unlike linear discriminant analysis (LDA), logit does not specify a cutoff point delineating bankrupt rms from nonbankrupt rms The model assigns each rm a probability of bankruptcy The decision makers can then choose a level that they are willing to tolerate The trade-off is between choosing a higher Type I or Type II error everal machine learning techniques were also used for prediction of bankruptcy Among the machine learning techniques used for prediction of bankruptcy are arti cial neural networks (ANNs) and genetic algorithms (GAs) Since these techniques do not rely on any distributional assumptions about the variables, they avoid problems associated with LDA and logit It has been found that although LDA provides superior results, GAs are effective tools for insolvency diagnosis, since GA results are obtained in less time and with fewer data requirements ANNs have been used for bankruptcy prediction and several researchers have reported a better performance of ANNs against statistical LDA and logistic approaches The foregoing discussion, about the value of correctly classi ed smart data to the enterprise, accepts the premise that all assets are ultimately expressed, described, and accounted for as data Examining this data can lead to cost savings and optimizing enterprise performance In this case, we showed how to improve the process of minimizing costs We examined capital and material and, through enterprise processes, produced outcomes that are higher yield products and results, whereby the processes operate under constraints and the work is performed by people and technologies performing in concert From the viewpoint of the enterprise head, the enterprise creates certain data that is critical to its performance. 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Mar 10, 2017 · This quick Java app uses the Tesseract library to help turn images into text. ... Topics: java ,tesseract ,image-to-text-conversion ,tutorial. Like (28). javascript ocr demo: Extract data off a card via a picture - APIs - Bubble Forum
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