Note that you need to set up the Amazon SageMaker environment to allow Amazon Comprehend to read from Amazon Simple Storage Service (Amazon S3) as described at the top of the notebook. Feel free to follow along while running the steps in that notebook. This post is accompanied by a Jupyter notebook that contains the same steps. After reading the structured output, we can visualize the label information directly on the PDF document, as in the following image. In particular, we train our model to detect the following five entities that we chose because of their relevance to insurance claims: DateOfForm, DateOfLoss, NameOfInsured, LocationOfLoss, and InsuredMailingAddress. Perform inference on an unseen document.īy the end of this post, we want to be able to send a raw PDF document to our trained model, and have it output a structured file with information about our labels of interest. ![]() Obtain evaluation metrics from the trained model. ![]() Use the PDF annotations to train a custom model using the Python API.We walk you through the following high-level steps: In this post, we walk through a concrete example from the insurance industry of how you can build a custom recognizer using PDF annotations. To address this, it was recently announced that Amazon Comprehend can extract custom entities in PDFs, images, and Word file formats. Until recently, however, this capability could only be applied to plain text documents, which meant that positional information was lost when converting the documents from their native format. This approach is flexible and accurate, because the system can adapt to new documents by using what it has learned in the past. To help automate and speed up this process, you can use Amazon Comprehend to detect custom entities quickly and accurately by using machine learning (ML). Rule-based software can help, but ultimately is too rigid to adapt to the many varying document types and layouts. Manually scanning and extracting such information can be error-prone and time-consuming. Insurance claims, for example, often contain dozens of important attributes (such as dates, names, locations, and reports) sprinkled across lengthy and dense documents. See the LICENSE file.In many industries, it’s critical to extract custom entities from documents in a timely manner. ![]() This sample code is made available under the MIT-0 license.
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