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Key Differences Between OCR and IDP?

To do this, businesses rely on modern technology, such as intelligent document processing (IDP) and optical character recognition (OCR). IDP and OCR play important roles in automated data extraction and document processing, although their capabilities and applications differ. In this blog post, we will look into the fundamental differences between IDP and OCR and their respective commercial benefits.

 

In the previous blog about the IDP, we focused on intelligent document processing, how it works, and other relevant details. We also briefly discussed the difference between OCR and IDP. But this blog will guide you in detail through the differences between OCR and IDP and their similarities. How the transformation took place and what the new iCustoms IDP offers.

What are OCR and IDP?

Optical character recognition was used in the early stages of reading the documents into the computer. To read the text and convert the machine-readable document from scanned photos or handwritten text. It is defined as:

 

OCR converts handwritten or scanned text into machine-readable text. It recognises and converts letter shapes, patterns, and combinations into editable and searchable data using algorithms and pattern recognition.

 

On the other hand, IDP, or intelligent document processing, is an advanced form of the OCR feature that uses artificial intelligence to read and extract the data. It is defined as:

 

Intelligent document processing software used for innovative document processing. It deliberately takes the documents, reads them, and also extracts the required information upon request.

Working of OCR

The character recognition process is straightforward and uses a simple framework. It includes the input, reading, and giving the output in the form of machine-readable language. Its workflow operates on the following steps:

 

  • Preprocessing: OCR software preprocesses images for analysis. Image improvement, noise reduction, deskewing, and normalisation guarantee optimal character identification.
  • Analysing Images: OCR detects text in images. It segmented lines, paragraphs, and characters from the background. This step uses pattern recognition and machine learning.
  • Analysing Images: The OCR algorithm then recognises characters within text regions. Recognition methods include feature extraction, template matching, statistical modelling, and artificial neural networks.
  • Post-processing: Post-processing by OCR software improves accuracy. Language modelling, context analysis, and error correction refine text recognition. This step fixes recognition issues and improves output.
  • Generating Results: Post-processing by OCR software improves accuracy. Language modelling, context analysis, and error correction refine text recognition. This step fixes recognition issues and improves output.

Process flow of IDP:

  • Document intake: IDP technology uses the document in the form of either a text file (PDF or Word) or images for formatting it. Email or document management platform connectors can upload these documents manually or automatically.
  • Document class: IDP classifies incoming documents. This process classifies documents by content, layout, or other properties using machine learning algorithms or established criteria.
  • Data extraction: Intelligent processing of documents provides better extraction according to one’s particular requirements. While developing the software, one can specify the documents and things that need to be extracted.
  • Data Validation: IDP systems evaluate extracted data against patterns, formats, and business rules using predefined rules, templates, or machine learning algorithms. This stage detects data errors.
  • Integration and Automation: Organisational systems and workflows use validated data. The IDP system automatically populates databases, ERP systems, and other applications with extracted data. Predefined rules or conditions can trigger processes, actions, or notifications.
  • Exception Handling: The IDP system flags and sends exceptions to humans. Poor quality documents, imprecise data fields, and unrecognised patterns are exceptions. To verify data, humans can evaluate and correct exceptions.
  • Analytical insights: The IDP system may analyse extracted data for insights and decision-making. Reports, trends, and data-driven analysis can be done with extracted data.
  • Enhancing learning: Using machine learning and AI, advanced IDP systems learn from user interactions, document feedback, and data validation. It improves accuracy, recognises new document types, and adapts to changing document structures.

Comparison between OCR and IDP

Optical character recognitionIntelligent document processing
OCR technology reads scanned or handwritten text.IDP extracts text and contextual information beyond OCR.
OCR only extracts text and does not automate operations.IDP automates end-to-end document-centric procedures using business process management solutions.
Complex document formats for fonts could result in OCR issues. IDP handles exceptions automatically. It handles OCR exceptions, reducing manual involvement.
OCR extracts text and offers searchable and editable data, but it does not provide insights or analytics.IDP analyses extracted data.
OCR works well for simple text extraction. Complex documents, tables, and unstructured data may challenge it.IDP handles tables, forms, and unstructured data. It better understands context and extracts data from various document types.

What is unique about iCustoms IDP?

Intelligent document processing offers its use according to specific requirements concerning the field. iCustoms has designed an IDP that is used for customs automation with accurate results. It accepts documents in the form of images and PDF files. The iCustoms IDP is user-friendly and super easy to use.

 

You can either use one service, like classification, or multiple services, according to your needs. The documents are saved for further use, and while extracting, it gives you a choice to choose any other important information that hasn’t been used earlier or is new from the pre-defined ones. 

 

Below are pictures of three screens that show the document processing working. The first image is the window with all the saved documents and their statuses. One important thing to remember is that you can use these documents repeatedly, i.e., they are certificates, and they can be used repeatedly if you just upload them once. 

 

Additionally, the second image shows the document upload status. As mentioned earlier, you can use the image or PDF file for it. The last image shows two sides: on the right side, the keywords will be extracted from the particular document. The left side shows the document with the highlighted parts to show what information it has acquired. You can choose any other keyword too, or remove it from the existing one.

Conclusion:

With the advent of IDP and OCR, document processing and data extraction have received a major technological boost. OCR lays the groundwork for digitising documents and automating basic data extraction; IDP expands upon this with its superior capabilities.

 

Organisations may make educated decisions about which option is best for them based on an awareness of the key distinctions and benefits of IDP and OCR. IDP and OCR can play critical roles in optimising processes and unlocking the true potential of digital transformation by increasing efficiency, enhancing data correctness, and achieving end-to-end automation.

FAQ's
Does IDP use OCR?

Yes, it surely uses OCR for reading the documents and converting them into machine learning text, as IDP is the advanced form of OCR, so it uses the basic concept of it.

Is OCR capable of data extraction?

No, OCR was just built to read and convert the text, including the special characters. For data, the IDP modules are in action.

What is OCR in a visa?

OCR uses machine-readable zones (MRZs) to extract visa data. MRZs on visas and passports carry alphanumeric machine-readable information.

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