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Information Extraction TEXT Sample Process (IeTEXTSample)

Information Extraction TEXT Sample Process (IeTEXTSample)

Overview

IeTEXT Sample performs automatic processing of TXT documents. The purpose of the process is to extract the following fields from the TXT 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


IeTEXT Sample Lifecycle includes:

Prerequisites

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

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

  2. Upload the IeTEXT 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-ietext-ap/<EasyRPA version>/easy-rpa-ietext-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-ietext-ap

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

    Module class: eu.ibagroup.sample.ml.ietext.IeTextSample

    Group Id: eu.ibagroup.samples.ap

    Artifact Id: easy-rpa-ietext-ap

    Version Id: <EasyRPA version>

IeText Sample Process Package structure

Artefact

Description

TEXT IE Sample

IeText Sample automation process

IE_TEXT_SAMPLE_DOCUMENTS

Datastore used for extraction automation process

IE_TEXT_SAMPLE

Information extraction document set. Contains order invoice test samples

TEXT IE Sample

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-ietext-ap-<EasyRPA version>.jar

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

html_ie_invoice_pre-<version>.tar

Information extraction from TEXT order invoices ML model

storage/data

Folder that contains documents to be uploaded in File Storage

Configuration Parameters for  IeTEXT Sample Automation Process:

Key

Default Value

Description

inputFolder

ietext_sample/input

File Storage folder where input documents are stored.

fileFilter

.*\.txt

Regular expression for files to select.

configuration

{
    "DEFAULT": {
        "dataStore": "IE_TEXT_SAMPLE_DOCUMENTS",
        "documentType": "TEXT IE Sample",
        "model": "text_ie_invoice_tabular_3",
        "runModel": "text_ie_invoice_tabular_3,1.4",
        "storagePath": "ietext_sample",
        "exportDocumentSet": "IE_TEXT_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 TXT document.

Step 2. Prepare Documents

On this step input data for ML model execution is prepared. As part of the preparation TXT→HTML conversion is employed (done in the HTML IE Document Processor itself - no additional setup is required).  Files created as a result of the original document processing are saved to the same ietext_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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