Automatically extract content from PDF files using Amazon Textract - AWS Prescriptive Guidance
SummaryPrerequisites and limitationsArchitectureToolsEpicsRelated resourcesAttachments

Automatically extract content from PDF files using Amazon Textract

Created by Tianxia Jia (AWS)

Environment: Production

Technologies: Machine learning & AI; Analytics; Big data

AWS services: Amazon S3; Amazon Textract; Amazon SageMaker

Summary

Many organizations need to extract information from PDF files that are uploaded to their business applications. For example, an organization could need to accurately extract information from tax or medical PDF files for tax analysis or medical claim processing.

On the Amazon Web Services (AWS) Cloud, Amazon Textract automatically extracts information (for example, printed text, forms, and tables) from PDF files and produces a JSON-formatted file that contains information from the original PDF file. You can use Amazon Textract in the AWS Management Console or by implementing API calls. We recommend that you use programmatic API calls to scale and automatically process large numbers of PDF files.

When Amazon Textract processes a file, it creates the following list of Block objects: pages, lines and words of text, forms (key-value pairs), tables and cells, and selection elements. Other object information is also included, for example, bounding boxes, confidence intervals, IDs, and relationships. Amazon Textract extracts the content information as strings. Correctly identified and transformed data values are required because they can be more easily used by your downstream applications. 

This pattern describes a step-by-step workflow for using Amazon Textract to automatically extract content from PDF files and process it into a clean output. The pattern uses a template matching technique to correctly identify the required field, key name, and tables, and then applies post-processing corrections to each data type. You can use this pattern to process different types of PDF files and you can then scale and automate this workflow to process PDF files that have an identical format.   

Prerequisites and limitations

Prerequisites 

  • An active AWS account.

  • An existing Amazon Simple Storage Service (Amazon S3) bucket to store the PDF files after they are converted to JPEG format for processing by Amazon Textract. For more information about S3 buckets, see Buckets overview in the Amazon S3 documentation.

  • The Textract_PostProcessing.ipynb Jupyter notebook (attached), installed and configured. For more information about Jupyter notebooks, see Create a Jupyter notebook in the Amazon SageMaker documentation.

  • Existing PDF files that have an identical format.

  • An understanding of Python.

Limitations 

Architecture

This pattern’s workflow first runs Amazon Textract on a sample PDF file (First-time run) and then runs it on PDF files that have an identical format to the first PDF (Repeat run). The following diagram shows the combined First-time run and Repeat run workflow that automatically and repeatedly extracts content from PDF files with identical formats.

Using Amazon Textract to extract content from PDF files

The diagram shows the following workflow for this pattern:

  1. Convert a PDF file into JPEG format and store it in an S3 bucket. 

  2. Call the Amazon Textract API and parse the Amazon Textract response JSON file. 

  3. Edit the JSON file by adding the correct KeyName:DataType pair for each required field. Create a TemplateJSON file for the Repeat run stage.

  4. Define the post-processing correction functions for each data type (for example, float, integer, and date).

  5. Prepare the PDF files that have an identical format to your first PDF file.

  6. Call the Amazon Textract API and parse the Amazon Textract response JSON.

  7. Match the parsed JSON file with the TemplateJSON file.

  8. Implement post-processing corrections.

The final JSON output file has the correct KeyName and Value for each required field.

Target technology stack  

  • Amazon SageMaker 

  • Amazon S3 

  • Amazon Textract

Automation and scale

You can automate the Repeat run workflow by using an AWS Lambda function that initiates Amazon Textract when a new PDF file is added to Amazon S3. Amazon Textract then runs the processing scripts and the final output can be saved to a storage location. For more information about this, see Using an Amazon S3 trigger to invoke a Lambda function in the Lambda documentation.

Tools

  • Amazon SageMaker is a fully managed ML service that helps you to quickly and easily build and train ML models, and then directly deploy them into a production-ready hosted environment.

  • Amazon Simple Storage Service (Amazon S3) is a cloud-based object storage service that helps you store, protect, and retrieve any amount of data.

  • Amazon Textract makes it easy to add document text detection and analysis to your applications.

Epics

TaskDescriptionSkills required

Convert the PDF file.

Prepare the PDF file for your first-time run by splitting it into a single page and converting it into JPEG format for the Amazon Textract synchronous operation (Syn API).

Note: You can also use the Amazon Textract asynchronous operation (Asyn API) for multipage PDF files.

Data scientist, Developer

Parse the Amazon Textract response JSON.

Open the Textract_PostProcessing.ipynb Jupyter notebook (attached) and call the Amazon Textract API by using the following code:

response = textract.analyze_document(
Document={
        'S3Object': {
            'Bucket': BUCKET,
            'Name': '{}'.format(filename)
                    }
                },
        FeatureTypes=["TABLES", "FORMS"])

Parse the response JSON into a form and table by using the following code:

parseformKV=form_kv_from_JSON(response)
parseformTables=get_tables_fromJSON(response)
Data scientist, Developer

Edit the TemplateJSON file.

Edit the parsed JSON for each KeyName and corresponding DataType (for example, string, float, integer, or date), and table headers (for example, ColumnNames and RowNames).

This template is used for each individual PDF file type, which means that the template can be reused for PDF files that have an identical format.

Data scientist, Developer

Define the post-processing correction functions.

The values in Amazon Textract's response for the TemplateJSON file are strings. There is no differentiation for date, float, integer, or currency. These values must be converted to the correct data type for your downstream use case. 

Correct each data type according to the TemplateJSON file by using the following code:

finalJSON=postprocessingCorrection(parsedJSON,templateJSON)
Data scientist, Developer
TaskDescriptionSkills required

Prepare the PDF files.

Prepare the PDF files by splitting them into a single page and converting them into JPEG format for the Amazon Textract synchronous operation (Syn API).

Note: You can also use the Amazon Textract asynchronous operation (Asyn API) for multipage PDF files.

Data scientist, Developer

Call the Amazon Textract API.

Call the Amazon Textract API by using the following code:

response = textract.analyze_document(
        Document={
        'S3Object': {
            'Bucket': BUCKET,
            'Name': '{}'.format(filename)
                    }
                },
        FeatureTypes=["TABLES", "FORMS"])
Data scientist, Developer

Parse the Amazon Textract response JSON.

Parse the response JSON into a form and table by using the following code:

parseformKV=form_kv_from_JSON(response)
parseformTables=get_tables_fromJSON(response)
Data scientist, Developer

Load the TemplateJSON file and match it with the parsed JSON.

Use the TemplateJSON file to extract the correct key-value pairs and table by using the following commands:

form_kv_corrected=form_kv_correction(parseformKV,templateJSON)
form_table_corrected=form_Table_correction(parseformTables, templateJSON)
form_kv_table_corrected_final={**form_kv_corrected , **form_table_corrected}
Data scientist, Developer

Post-processing corrections.

Use DataType in the TemplateJSON file and post-processing functions to correct data by using the following code: 

finalJSON=postprocessingCorrection(form_kv_table_corrected_final,templateJSON)
Data scientist, Developer

Related resources

Attachments

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