Tasks

Analyze Document

The Analyze Document task provides the capability to perform Optical Character Recognition (OCR) on PDFs and images, enabling the extraction of text from these formats. Additionally, it offers advanced analytics features that allow you to further process, analyze, and gain insights from the extracted data, making it a powerful tool for handling and interpreting document-based information.

Use Cases

• Use this task to extract text from a document
• Use to extract a data structure of all entities in a filled in form
• Use to extract data from a South African ID document
• Use to classify a proof of payment document as belonging to a specific bank in South Africa

Basic usage

Parameters

document_processor_type

The type of analysis that you would like to perform on the file.

Current Options are:

• ocr
  Extract text from a document with optical character recognition. The document can be a pdf or an image.
• entity_extraction
  Extract entities and form fields from a document. The document can be a pdf or an image.
• id_proofing
  Id proof a document, getting verification signals on image manipulation, verification of an id document and checking for suspicious words. The document can be a pdf or an image.
• classifier
  Classify a proof of payment document as belonging to a specific bank in South Africa. The document can be a pdf or an image.

ocr

This object key must correspond with the document_processor_type. This object will contain all the options and params for the selected document_processor_type

files

An array of file objects. Each object contains file meta data.
The most important key for these objects is the fileuuid

Result

Properties

payload

The returned payload

total_pages

Number of pages analyzed

ocr

Contains all the analysis data for the ocr document_processor_type. This object with change to correspond to the document_processor type.

ocr.full_text

Contains all the text the analysis could get from the file

ocr.detected_languages

An array of objects. Each object in the array is a page of the file and indicates which languages were detected on that page of the document.

ocr.all_languages

An array containing language codes for all languages detected in the document.

OCR

Shown in the Basic Usage

Entity Extraction

This document_processor_type processor type is used to extract entity and form fields from a document.

Example would be a document that has an organization and fields that have information in them like a receipt, invoice, application form, etc.

It will attempt to identify the field name and the value of that field and create a key value pair. It will do this for each form field that it detects.

Here is an example of a form. Its a Fax Cover Sheet with a form with fields and entries for each field.

On the left is what it has extracted from the image displayed on the right.

You will notice the document_processor_type is now entity_extraction and so there should be the corresponding object with the same name referring to the file it should analyze.

Result

The result returned would look something like this. The returned data is quite large so only showing a sample of the important data that is returned.

The result payload is similar to the first example with a few additions:

entity_extraction

Contains all the analysis data for the ocr document_processor_type. This object with change to correspond to the document_processor type.

Please see above OCR example for explanations for the detected_languages and full_text objects & key values.

entity_extraction.entities

Contains an object for each entity detected. eg. organization is: Staples

entity_extraction.form_fields.pages

Contains an object for each pages.

Each page is an object in the array and each page object contains an object for each field detected. eg. Date: 04/12/2021 From: Jane Doe

ID Proofing

This is used to extract all the information from South African Identity documents, ie. Green bar code ID Book, Smart ID Card or Passports.

It doesn't only extract the information, it also does fraud checks to indicate if the document is valid or fraudulent.

You will notice the document_processor_type is now id_proofing and so there should be the corresponding object with the same name referring to the file it should analyze.

Result

The result returned would look something like this. The returned data is quite large so only showing a sample of the important data that is returned.

The result payload is similar to the first example with a few additions:

id_proofing

Contains all the analysis data for the id_proofing document_processor_type. This object with change to correspond to the document_processor type.

Please see above OCR example for explanations for the detected_languages and full_text objects & key values.

id_proofing.id_proofing_signals

Contains an entry for each id proofing check performed and its result. eg. fraud_signals_suspicious_words: PASS

This indicates the the suspicious words check passed inspection. No suspicious words found.

id_proofing.all_signals_passed

Indicates if all the id proofing checks passed. If one check fails, this will be false.

Classifier