OCR Data Extraction in AEM Forms

Infodales Tech Solutions | June 09, 2023

OCR Data

OCR stands for Optical Character Recognition. OCR data refers to the output generated by OCR software or systems when they scan and convert printed or handwritten text into digital text that computers can understand and process. This data typically includes the recognized text itself, along with information about its formatting, layout, and structure.
OCR technology has advanced significantly in recent years, allowing for high accuracy in converting scanned documents, images, or even live text from a camera feed into editable and searchable text. OCR data is valuable in various applications, such as digitizing books and documents, extracting information from forms, enabling text search in scanned documents, and aiding visually impaired individuals in accessing written content.

OCR Data Extraction in AEM Forms

Optical Character Recognition (OCR) integration in Adobe Experience Manager (AEM) Forms can significantly streamline data extraction from scanned documents or images. Choose a suitable OCR engine based on your requirements. Adobe Acrobat provides OCR capabilities, or you can opt for third-party OCR engines like Tesseract, ABBYY FineReader, or Google Cloud Vision API.

Integrate the chosen OCR engine with AEM Forms. This might involve installing plugins, libraries, or APIs provided by the OCR engine provider. Allow users to upload scanned documents or images through AEM Forms. Preprocess the uploaded documents/images if necessary. This may include tasks like image enhancement, noise reduction, or deskewing to improve OCR accuracy. Utilize the integrated OCR engine to extract text from the uploaded documents/images. This step involves passing the document/image to the OCR engine and receiving the extracted text.

There are several organizations offering OCR services, and if they have well-documented REST APIs, integrating them with AEM Forms using the data integration capability becomes straightforward. For the sake of this tutorial, we'll showcase OCR data extraction using ID Analyzer for uploaded documents."
This update maintains clarity and aligns with the tutorial's purpose, focusing on the integration with ID Analyzer for OCR data extraction.

Create a Swagger File

Creating a Swagger file involves defining your API's structure, endpoints, parameters, responses, and other details using the Swagger/OpenAPI Specification. Here's a step-by-step guide to help you create a Swagger file:

Understand Swagger/OpenAPI Specification
Choose a Swagger Editor
Define API Info
Define Paths and Operations
Define Parameters and Responses
Add Security Definitions (if needed)
Export and Save
Validate and Test

Use tools like Swagger Inspector or Postman to validate and test your Swagger file against your actual API endpoints.

						
							swagger: '2.0'

							info:

  							version: 1.0.0

  							title: Simple API

  							description: Learning Swagger

							host: api.idanalyzer.com

							schemes:

  							- https

							paths:

  							/:

    						post:

      						summary: Decode Documents

      						produces:

        					- application/json

      						consumes:

        					- application/x-www-form-urlencoded

      						operationId: Decode Documents

      						parameters:

        					- in: formData

          					name: file_base64

          					type: string

          					description: Base 64 image of the Documents

       						- in: formData

          					name: apikey

          					type: string

         					 description: API Secret Key

      						responses:

        					'200':

          					description: Successfull Response

          					schema:

            				$ref: '#/definitions/returnvalue'

							definitions:

  							result:

    						type: object

    						properties:

      						fullName:

        					type: string

      						documentNumber:

        					type: string

      						address:

        					type: string

      						dob:

        					type: string

      						pincode:

        					type: string

  							returnvalue:

    						type: object

    						properties:

      						result:

        					type: object

        					$ref: '#/definitions/result'

Create a Data Source

To connect AEM/AEM Forms with third party api's, you first make a data source in cloud services. You can use the Swagger file to set up this data source.

Log in to AEM and go to the Dashboard.
From Tools, select Cloud Services.
Pick or create a folder in Cloud Services to store your data sources.
Define settings like data type, endpoint URL, and authentication.
Save the data source

Create a Form Data Model

Creating a form data model in AEM Forms involves defining the structure of your form data, including the fields, data types, and validation rules. Here's a step-by-step guide:

Select you data source

Create a Client Lib

To proceed, we'll require the base64 encoded representation of the uploaded document. This encoded string serves as a crucial parameter in our REST invocation process.

Create an Adaptive Form

Maximize the potential of your adaptive form by integrating the POST invocations of the Form Data Model. Effortlessly extract valuable data from user-uploaded documents by leveraging this powerful feature. Transmit the base64 encoded string of the uploaded document securely through the form data model's POST invocation, ensuring smooth and efficient data extraction processes. Enhance your adaptive form's functionality and elevate your data collection capabilities with this seamless integration.

By integrating the POST invocations of the Form Data Model, extracting data from user-uploaded documents becomes a seamless process in adaptive forms. Leveraging the form data model's capabilities allows for efficient transmission of base64 encoded strings, enhancing data extraction and processing. With this integration, adaptive forms are empowered to deliver enhanced functionality and streamlined data collection experiences.

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Nitish Bisen | AEM Developer