Cell phones have an advantage with orientation sensors. But when the images are manually captured with the cameras or any digital sorts, they have different issues like Orientation and Skewness. Orientation and Skewness(Rotation) - Scanned documents or ID cards are usually parallel to the plane of the sensor. In multilingual situations, capturing information from the scanned documents stays as a primary research issue since information in complex symbolism is more troublesome. Sometimes few ID cards are printed in different languages. If at all the model is trained with a random set of images, there is a high probability of the model to underperform. Characters of various fonts have large within-class variations and form many pattern sub-spaces, making it difficult to perform accurate recognition when the character class number is large. Various Designs, Templates and Multilingual Environments - There are several fonts, designs, and templates for different types of ID cards. 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.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.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. Information extraction - We can capture all the information provided on the ID card and push that data as a unique source for further use.Here are a few reasons why digitization of information is becoming prevalent. and enter it into a data entry software.Īs deep learning approaches and OCR technologies have progressed, semi or fully automated solutions relating to physical document information extraction are seeing a wider adoption. 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. 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.
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