Textractor

textractor helps speed up PoCs by allowing you to quickly extract text, forms and tables from documents using Amazon Textract. It can generate output in different formats including raw JSON, JSON for each page in the document, text, text in reading order, key/values exported as CSV, tables exported as CSV. It can also generate insights or translate detected text by using Amazon Comprehend, Amazon Comprehend Medical and Amazon Translate. It takes advantage of Textract response parser library to easily consume JSON returned by Amazon Textract.

Prerequisites

• Python3
• AWS CLI

Setup

• Download code and unzip on your local machine.

Usage

Format:

• python3 textractor.py --documents [file|folder|S3Object|S3Folder] --text --forms --tables --region [AWSRegion] --insights --medical-insights --translate [LanguageCode]

Examples:

• python3 textractor.py --documents mydoc.jpg --text
• python3 textractor.py --documents ./mydocs/ --text --forms --tables
• python3 textractor.py --documents s3://mybucket/mydoc.pdf --text --forms --tables
• python3 textractor.py --documents s3://mybucket/myfolder/ --forms
• python3 textractor.py --documents s3://mybucket/myfolder/ --text --forms --tables --region us-east-1 --insights --medical-insights --translate es

Path to a folder on local drive or S3 bucket must end with /

Only one of the flags (--text, --forms and --tables) is required at the minimum. You can use combination of all three.

--region is optional. us-east-1 is default for local files/folder. For documents in S3, region of S3 bucket is selected as default AWS region to call Amazon Textract.

--insights, --medical-insights and --translate are optional.

Generated Output

Tool generates several files in the format below:

Text, forms and tables related output files

• document-response.json: Raw JSON response of Amazon Textract API call.
• document-page-n-response.json: Raw JSON blocks for each page document.
• document-page-n-text.txt: Detected text for each page in the document.
• document-page-n-text-inreadingorder.txt: Detected text in reading order (multi-column) for each page in the document.
• document-page-n-forms.csv: Key/Value pairs for each page in the document.
• document-page-n-tables.csv: Tables detected for each page in the document.

Insights related output files

• document-page-n-insights-entities.csv: Entities in detected text for each page in the document.
• document-page-n-insights-sentiment.csv: Sentiment in detected text for each page in the document.
• document-page-n-insights-keyPhrases.csv: Key phrases in detected text for each page in the document.
• document-page-n-insights-syntax.csv: Syntax in detected text for each page in the document.
• document-page-n-medical-insights-entities.csv: Medical entities in detected text for each page in the document.
• document-page-n-medical-insights-phi.json: Phi in detected text for each page in the document.
• document-page-n-text-translation.txt: Translation of detected text for each page in the document.

Arguments

Argument	Description
--documents	Name of the document or local folder/S3 bucket
--text	Extract text from the document
--forms	Extract key/value pairs from the document
--tables	Extract tables from the document
--region	AWS region to use for Amazon Textract API call. us-east-1 is default.
--insights	Generate files with sentiment, entities, syntax, and key phrases.
--medical-insights	Generate files with medical entities and phi.
--translate	Generate file with translation.

Source Code

• textractor.py is the entry point. It parses input arguments, and query S3 or local folder to get input documents. It then iterates over input documents and use DocumentProcessor to get response from Amazon Textract APIs.
• OutputGenerator takes Textract response and uses Textract response parser to process response and generate output.
• Example below shows how response parser library helps process JSON returned from Amazon Textract.

# Call Amazon Textract and get JSON response
docproc = DocumentProcessor(bucketName, filePath, awsRegion, detectText, detectForms, tables)
response = docproc.run()

# Get DOM
doc = Document(response)

# Iterate over elements in the document
for page in doc.pages:
    # Print lines and words
    for line in page.lines:
        print("Line: {}--{}".format(line.text, line.confidence))
        for word in line.words:
            print("Word: {}--{}".format(word.text, word.confidence))
    
    # Print tables
    for table in page.tables:
        for r, row in enumerate(table.rows):
            for c, cell in enumerate(row.cells):
                print("Table[{}][{}] = {}-{}".format(r, c, cell.text, cell.confidence))

    # Print fields
    for field in page.form.fields:
        print("Field: Key: {}, Value: {}".format(field.key.text, field.value.text))

    # Get field by key
    key = "Phone Number:"
    field = page.form.getFieldByKey(key)
    if(field):
        print("Field: Key: {}, Value: {}".format(field.key, field.value))

    # Search fields by key
    key = "address"
    fields = page.form.searchFieldsByKey(key)
    for field in fields:
        print("Field: Key: {}, Value: {}".format(field.key, field.value))

Cost

• As you run this tool, it calls different APIs (Amazon Textract, optionally Amazon Comprehend, Amazon Comprehend Medical, Amazon Translate) in your AWS account. You will get charged for all the API calls made as part of the analysis.

Other Resources

• Large scale document processing with Amazon Textract - Reference Architecture
• Batch processing tool
• JSON response parser

License

This library is licensed under the Apache 2.0 License.