![]() After sending the invoices to Form Recognizer, the files are run through the machine learning model, and scored results are sent back in JSON format. The PDF files used here are located within an Azure blob storage container, meaning everything thus far is being done on the cloud, allowing for greater scalability and security. I create a Databricks notebook that performs each stepwise task within a chunk of code. Enter Azure Databricks, a data analytics platform that leverages Microsoft's cloud resources and the Apache Spark language. ![]() To scale up this process, we need a tool that that can batch files, send them to Cognitive Services with the right credentials, collect results, and then save those results for later analysis. Documents can also be sent in batches to Cognitive Services via an API call and returned as scored results. The below example shows the Form Recognizer UI extracting data from a single, handwritten invoice. Form Recognizer even includes an Optical Character Recognition (OCR) to identify handwritten text. A user can select any of these models or use a generic one to extract text from another document type, such as a letter. It has some specific models that were trained on common use cases, such as invoices, receipts, business cards and IDs. There is also a set of computer vision models and importantly, for our purposes, Form Recognizer.įorm Recognizer extracts text from a variety of file types. This key ingredient is a series of pretrained machine learning models that cover a variety of areas, from text analytics to speech translation. The secret sauce behind data extraction at scale features Azure Cognitive Services. We will look at these in turn, along with the tools that make them possible. The below diagram outlines the process with three components: extraction, orchestration, and visualization. Digitization may be a clear first step, but what comes after that? Let's consider one such scenario, where a company wants to identify insights from a set of text-embedded and scanned invoices. ![]() It's all too common for an organization to have business-critical information on rigid file types or even paper. In this article, I will demonstrate a set of tools that can extract, compile, and visualize data from large swaths of intractable files - proving that even data on a PDF can have a second life. However, this feature can be a failure when trying to pull information from important documents. I once heard a conference presenter quip that "the PDF is where data goes to die." There is some truth in the aphorism, as PDF format is often used to make text unalterable. ![]()
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