There can be a good many physical and technical errors, out of which a few are discussed below. Talk to a Nanonets AI expert to learn more.ĭeep learning has been a remedy to many automation problems, but there are still several challenges researchers and developers face trying to build a perfect model that possesses commendable quality and outputs high accuracy. Nanonets OCR API has many interesting use cases. For example, the model that is trained to identify information from a particular ID card can be deployed on a website where users upload the images in bulk, or it can be used in mobile phones where users click on images and thereby, the information is extracted. Integrates easily into any system - Digitized solutions can be easily integrated into any system.The chances of human error can be reduced by automating repetitive tasks and allowing humans to review document information on the final stages of the information extraction pipeline. Records data without error - With the advancement in technologies and computational power, machines are now capable of capturing the data without many errors.
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This shift towards fast digital processes instead of manual entry and review is enabled by deep learning based approaches (discussed later). It takes a couple of seconds to simply scan an ID card and retrieve all the data from it. Greater speed and efficiency - Digitizing ID cards can save a lot of time and money for businesses and organizations.
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This helps to maintain data in an organized fashion and facilitates any sort of verification or registration process. All the information pulled from the captured ID card will be in a simple text/numerical format.
The customer is expected to submit a digital copy of the documents which a manual reviewer will review, identify if it is fake, extract information like name, address, etc. This process is usually time consuming and prone to errors since it is done manually at a lot of places. A few examples include banks and insurance companies. Impeller object.Many organizations during their onboarding procedures, to have adequate amount of information about their customers, requires customers to submit some documents that they could use to verify their identity and get relevant details about them. Parameters file str or pathlib.Pathįilename to which the data is saved. export_to_excel ( path_name = None ) ¶Įxport curves to excel file. Impeller map according to these arguments. Given a speed this method will calculate a curve in the curve ( speed = None ) ¶Ĭalculate specific point in the performance map. The new impeller with the converted performance map for the required We calculate new values of speed for each curve and the respective dischargeĬondition. The method in which the curves will be converted.įor now only ‘speed’ is implemented, which means that, based on volume ratio, The new suction condition to which we want to convert to.
Parameters original_impeller ccp.Impeller Methods _init_ ( points ) ¶ classmethod convert_from ( original_impeller, suc = None, find = 'speed' ) ¶Ĭonvert performance map from an impeller.
The created instance will hold the dimensional points used in instantiation.Ĭurves will be generated from points close in similarity. Impeller instance is initialized with the list of points.
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