Document AI application

A) Database design: Determine the structure of your database based on the information you want to store. Identify the necessary tables and the relationships between them. For example, you might have a table for suppliers, one for customers, one for documents (invoices, orders), one for products, and so on. Define the attributes of each table and the primary and foreign keys to establish the relationships between them. B) Database creation: Use a relational database management system (RDBMS) such as MySQL, PostgreSQL, or Microsoft SQL Server to create your database. Follow the specific instructions of the chosen RDBMS to create the tables, define the attributes and relationships. C) Data extraction: Use the information extraction techniques described earlier to extract the relevant data from the documents. You can use machine learning methods or other techniques to extract the desired information and associate it with the corresponding database tables. D) Data insertion into the database: Use SQL language or a data import tool provided by your RDBMS to insert the extracted data into the database. Use INSERT statements to insert records into the appropriate tables, following the defined relationships. E) Quality control: Verify the correctness of the data inserted into the database. Run queries to extract the data and compare it with the original documents to ensure the accuracy of the extracted information. F) Data querying and analysis: Use SQL language to query the database and retrieve data based on specific criteria. You can run complex queries to extract detailed information, generate reports, or analyze the data for decision-making purposes. G) Updating and maintenance: If the Document AI project is ongoing and new documents are being extracted regularly, you will need to plan for updating the database with the new data. You can create scripts or automated procedures for inserting the newly extracted data into the existing database. [login to view URL]