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AI Skills in Appian | Document Extraction in Appian | Appian Tutorials for Beginners

Education

Introduction

In today's tutorial, we will delve into the fascinating area of AI skills in Appian, specifically focusing on document extraction. Understanding how AI can automate and enhance document processing is crucial for anyone looking to improve efficiency in their organization.

Overview of AI Skills

AI skills in Appian can be broadly categorized into two main functions: document classification and document extraction.

Document Classification

Document classification involves categorizing different types of documents into specific segments. For instance, you might have documents related to orders, invoices, or payment confirmations. Using AI classification, you can streamline the process of identifying and organizing these documents.

Document Extraction

On the other hand, document extraction is concerned with retrieving specific data from a document. For example, if you have an invoice, you may wish to extract details such as the name, invoice number, invoice date, and total amount. Today, we will focus on the extraction part of the process.

Getting Started with Document Extraction

Let’s first create a model for document extraction in Appian. Here’s a step-by-step guide:

  1. Creating a Model: Navigate to the AI skills section in Appian and select the "Create First Model" option. You will be presented with two document types: semi-structured and highly structured. For today, we will select highly structured documents.

  2. Uploading Documents: Before uploading your documents, note that you must upload at least 10 unique PDF files (maximum of 200, each file under 7 MB). The documents should not repeat any invoice numbers or other identifying characteristics.

  3. Defining Extraction Fields: After uploading the documents, you will need to specify which fields you want to extract. Common fields for invoices include:

    • Name
    • Address
    • Invoice Number
    • Invoice Date
    • Total Amount

  4. Mapping Fields: Next, you will map the extracted fields to their corresponding columns in your output data structure. This ensures that each extracted value is saved accurately.

  5. Training the Model: Save your changes, then train the model. The model will learn from the documents you uploaded and will start extracting data as per your specifications. Keep in mind that the training process may take some time.

  6. Creating a Process Model: Once the model is trained, move on to create a process model to handle document extraction and save the extracted data into your records.

  7. Processing Documents: Validate that your model works correctly by processing the uploaded documents. You should receive feedback on the success of the extraction process, which indicates the accuracy of the fields identified.

  8. Monitoring Progress: You can monitor the status of your extraction model and check for any errors or required adjustments by looking into the monitor tab.

  9. Improving Accuracy: If the accuracy of the extracted fields is not sufficient, consider re-training the model with additional documents to help improve its performance.

Conclusion

Document extraction in Appian is a powerful tool that can save you substantial time and effort by automating the data retrieval process. With continued use, the AI model can increase its accuracy, ultimately benefiting your operations.

Keywords

  • Appian
  • AI Skills
  • Document Extraction
  • Document Classification
  • Invoice Extraction
  • Automation
  • Data Retrieval
  • Process Model

FAQ

  1. What is the difference between document classification and document extraction in Appian?

    • Document classification is about categorizing documents into types, while document extraction focuses on retrieving specific data from those documents.

  2. What types of documents can I upload for extraction?

    • You can upload PDF documents only, with a minimum of 10 unique files and a maximum of 200 files, each not exceeding 7 MB.

  3. How do I improve the accuracy of my extraction model?

    • You can improve accuracy by training the model with more diverse and numerous documents over time to help it learn better.

  4. What fields can I extract from a document?

    • Common fields include name, address, invoice number, invoice date, and total amount, though you can specify other fields as needed.

  5. How can I monitor the progress of my extraction model?

    • You can check the status and any errors by navigating to the monitor tab in Appian where the model is processed.