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Information Extraction HTML Sample Process (IeHTMLSample)

Information Extraction HTML Sample Process (IeHTMLSample)

Overview

IeHTML Sample performs automatic processing of HTML documents. The purpose of the process is to extract the following fields from the HTML order invoice documents:

  • Invoice Number

  • Invoice Date

  • Due Date

  • Company Name

  • Street Address

  • City

  • Zip Code

  • Phone Number

  • Email

  • Product Name

  • Product Description

  • Quantity

  • Price

  • Tax Rate

  • Discount Rate

  • Total Discount

  • Total Amount

IeHTML Sample Lifecycle includes:

Prerequisites

In order to successfully set up and run IeHTML Sample Process:

  1. Ensure that you have a running node with the "AP_RUN" capabilities.

  2. Upload the IeHTML Sample package to the Control Server. The package can be found in the following directory: http://<CS host>/nexus/repository/rpaplatform/eu/ibagroup/samples/ap/easy-rpa-iehml-ap/<EasyRPA version>/easy-rpa-iehml-ap-<EasyRA version>-bin.zip
    The source code can be found here:  https://code.easyrpa.eu/easyrpa/easy-rpa-samples/-/tree/dev/easy-rpa-ml-aps/easy-rpa-iehml-ap

  3. Ensure the following details are provided for the IeHTML automation process in the Automation Process Details tab

    Module class: eu.ibagroup.sample.ml.iehtml.IeHtmlSample

    Group Id: eu.ibagroup.samples.ap

    Artifact Id: easy-rpa-iehml-ap

    Version Id: <EasyRPA version>

IeHTML Sample Process Package structure

Folder

Description

IeHTML Sample

IeHTML Sample automation process

HTML IE Document Processor

Standard HTML information extraction automation process

HTML_IE_INVOICE

Information extraction document set. Contains order invoice test samples

HTML IE Invoice

Invoice information extraction document type. Defines the entities to be extracted from order invoices.

HTML Information Extraction Task

Information Extraction human task type. Defines the task input form in the Workspace

easy-rpa-iehml-ap-<EasyRPA version>.jar

Root archive and dependencies. Contains code of IeHTML Sample automation process

invoice_for_orders_240-<version>.tar.gz

Information extraction from HTML order incoices ML model

storage/data

Folder that contains documents to be uploaded in File Storage

Configuration Parameters for IeHTML Sample Automation Process:

Key

Default Value

Description

inputFolder

iehtml_sample/input

File Storage folder where input documents are stored.

fileFilter

.*\.html

Regular expression for files to select.

configuration

{
    "html-ie": {
        "dataStore": "IE_HTML_SAMPLE_DOCUMENTS",
        "documentType": "HTML IE Sample",
        "model": "invoice_for_orders_240",
        "runModel": "invoice_for_orders_240,1.1",
        "storagePath": "iehtml_sample",
        "exportDocumentSet": "IE_HTML_SAMPLE",
        "bucket": "data"
    }
}

dataStore - the datastore name where to store input documents

documentType - the document type name to use for classification.

runModel - the model name and version to run

storagePath - the document path on a storage to use

Json parameter that provides mapping of document types and corresponding ML models and contains model name, model version and document type name of each model. 

Included Steps

Step 1. Ingest Documents

RPA bot extracts documents from the dedicated folder in File Storage. It compiles a list of documents to be processed. The status of the document which has just been extracted for processing is 'NEW'.

When a list of documents has been generated RPA bot prepares batches of documents for processing. The number of documents in a batch is determined by the configuration parameter batchSize.

After this step a separate workflow of RPA and ML tasks is created for each document.

Step 2. Prepare Documents

On this step input data for ML model execution is prepared. Files created as a result of the original document processing are saved to the same iehtml_sample File Storage folder where the original document is stored.

Step 3. Extract Data

The ML Information Extraction model is employed to extract the specific fields from HTML invoice order documents.

Step 4. Verify Extracted Data

This step enables human verification and corrections to ensure accuracy of data extracted. After the relevant business entities have been extracted from a document, a human task is created and needs to be completed in Workspace. It contains ML Information Extraction model output that humans can review, validate and correct.

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