Welcome to the future of documents, with Google Document Ai, make the most out of your unstructured data within the documents
How document ai can help you make sense of your data so that you can go from unstructured or dark data to use structured data using ai
There is a huge quantity of data in the papers that are used by organisations all over the world to communicate information. The issue with this sort of data is that it is unstructured or dark data. Just consider all the pdfs, emails, forms, and contracts that you engage with on a daily basis. Dark data is information that companies regularly obtain, analyse, and store but seldom utilise for other reasons; in other words, companies are sitting on a data goldmine that might be used to gather analytics or automate operations if it could be turned into a machine-readable format.
There are three ways that businesses may now extract data from documents. The first is manual data entry, which involves having people read the papers and then manually entering the data they observe into a system. This technique is time-consuming and prone to errors. Optical character recognition, or OCR, technology can be used to parse documents with a fixed layout and extract the text using this method. It can be useful, but the types of documents that can be processed are limited. The third option is to analyse documents and extract information using artificial intelligence and machine learning.
Using these applications in the cloud also enables flexible scalability as the volume of documents changes. Artificial intelligence and machine learning technology have advanced rapidly in recent years, making it now possible to use them to read documents, parse the content, and extract valuable information from a variety of document types. This helps remove much of the data entry labour and can speed up document processing. Because it incorporates a variety of diverse approaches and algorithms, such as entity extraction from natural language, machine translation, and data loss prevention, document comprehension is one of the most complicated areas of machine learning.
This is where document ai, a managed service offered by Google Cloud, enters the picture. Doc ai helps you convert your unstructured text into structured data. A whole cloud-based platform for processing documents is called Document AI. In addition to reading and adjusting your documents, it also comprehends their spatial organisation. For instance, if you run a generic form through a form parser, it will detect that your form has questions and answers and provide those to you as key-value pairs. With this data in a structured format, you can start using it. Perhaps you want to run analytics on customer feedback, process lengthy, multi-page application forms, or try to add more data sources to your dashboards. With document data in a structured format, you can easily incorporate it into your applications by calling an api; no data science expertise is now necessary.
The basic document AI in Google Document AI is made to operate with pretty much any document you can throw at it. It contains ocr, a structured form parser, and document quality analysis. For more specific document formats, Google provides pre-built models. Google trains and maintains the general and specialised models so you don't have to, and soon the platform will also allow you to build models for your own document types. You'll be able to train custom models from scratch or uptrain existing models without writing any machine learning code. Google has models for standardised forms like driver's licences etc. as well as models for high variance document types like invoices and receipts.
Google Document AI is fixing the problem which the world has and as organisations are transforming to the new digital age, they cannot let go of the important information that is from the old ages. Digitalising the end-to-end information require a product like Document AI so that everything which is offline need to get online, not only online but also intelligent.
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