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Tesseract OCR – opensource .google.com
Tesseract is an OCR engine with support for unicode and the ability to recognize more than 100 languages out of the box. It can be trained to recognize other ...

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 Several 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 ritical to its performance.



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What is the Significance of the Release of a Free OCR Sdk Engine? The introduction of SDKs or "software development kits" has been one of the most significant ...

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ython is widely used when building websites and serves several different roles in this capacity First, Python scripts are often a useful way to simply generate a set of static HTML pages to be delivered by a web server For example, a script can be used to take raw content and decorate it with additional features that you typically see on a website (navigation bars, sidebars, advertisements, stylesheets, etc)This is mainly just a matter of file handling and text processing topics that have been covered in other sections of the book Second, Python scripts are used to generate dynamic content For example, a website might operate sing a standard webserver such as Apache but would use Python scripts to dynamically handle certain kinds of requestsThis use of Python is primarily associated with form processing For example, an HTML page might include a form like this:.

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Within the form, the ACTION attribute names a Python script 'subscribepy' that will execute on the server when the form is submitted nother common scenario involving dynamic content generation is with AJAX (Asynchronous Javascript and XML)With AJAX, JavaScript event handlers are associated with certain HTML elements on a page For example, when the mouse hovers over a specific document element, a JavaScript function might execute and send an HTTP request to the webserver that gets processed (possibly by a Python script)When the associated response is received, another JavaScript function executes to process the response data and displays the resultThere are many ways in which results might be returned For example, a server might return data as plaintext, XML, JSON, or any number of other formats Here is an example HTML document that illustrates one way to implement a hover popup where moving the mouse over selected elements causes a popup window to appear.

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Tesseract Source Code Documentation. Version 3.04.01 · Version 3.05.02 · Version 4.0.0.

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