A subject of debate that organizations are often locked in is how Intelligent Document Processing [IDP] offering differs from the traditional optical character recognition [OCR] solutions. With so many acronyms floating around, people wonder if IDP is just the latest iteration of OCR. Since most IDP solutions incorporate OCR in certain aspects, making proper distinctions a challenging job.
Extracting data from documents has become a mundane task of several tech jobs today. To perform this task, you have three choices -
1. Manual data extraction
3. IDP (Intelligent Data Process)
While manual extraction of data from documents can get laborious and yield lower accuracy and OCR has its limitations with colored backgrounds, glaring, and improper data structuring, people have started turning towards IDP.
IDP vs. OCR - Definitions and Insights
Optical Character Recognition converts a scanned image into text by transcribing it one character at a time. OCR has evolved over time, and now has the potential to extract text from a plethora of languages.
A side shoot of OCR is Intelligent Character Recognition (ICR), which works similar to OCR, except that this tool helps capture handwritten characters with one character at a time.
ICR relies on a constrained handprint that helps segregate handwritten characters into individual boxes. Most forms do not get designed for ICR, which makes automation a tedious job for ICR. ICR also stumbles when transcribing normal handwriting or cursive and requires manual data entry.
Intelligent Document Processing is any software solution that captures information from documents such as email text, PDF, or scanned documents. It then classifies and extracts relevant data for further processing through AI technologies.
Leading IDP solutions utilize sophisticated technology and AI to enhance the quality of the scanned documents by providing features such as noise reduction. They then capture the information and classify it subsequently.
You can seamlessly integrate these solutions with internal applications, systems, as-well-as other automation platforms. IDP enjoys a wide variety of use cases across several business functions such as claims processing, record management compliance, and client onboarding.
Transcription of different document types through OCR and IDP
OCR merely transcribes a document and provides you with a text representation of the image but fails to provide the necessary content for downstream processes. Another domain where OCR exhibits shortcomings is its incompatibility with various document types.
An IDP solution is an upgraded version of OCR and helps extrapolate the business data from a document. It has the potency to handle more practical challenges such as different document types.
Here is a comprehensive comparison of how both OCR and IDP interpret document types and yield final output to the user -
1. PDF Invoice
A PDF invoice is machine-generated, that contains printed text and is commonly seen in a company when dealing with relevant credentials. Here is how it gets transcribed via OCR and IDP -
- When transcribing a PDF invoice, most OCR tools use the text layer without performing actual OCR, use the text layer to assist the functioning of OCR, or swap out the text layer if it was not electronically-generated.
- IDP utilizes several tools to capture information from the document, categorizes it accordingly, and extracts and organizes the data, which is sent downstream for AI processing.
This scanned document got filled out in sloppy handwriting and was marginally skewed when the bank received it for processing. Here is how it gets transcribed via OCR and IDP -
- It is factual that while half of the documents are still handwritten, the OCR/ICR systems are incompetent in handling the variability and sloppy handwritten text, adding to the workload for employees who have to review and then manually enter all the data.
- IDP enhances the image quality of every page automatically and then categorizes documents as per their user-defined taxonomies. With the aid of computer vision and deep learning models, IDP discerns handwriting exceedingly better than OCR.
Checks must get transcribed with greater accuracy as it involves financial matters. Here is how it gets transcribed via OCR and IDP -
- OCR can interpret the payor's address, check number, and MICR (routing/banking info) but fails to capture the handwriting under the date, CAR (written amount in numbers), and LAR (written out amount in words) columns.
- IDP employs specialized models to boost extraction automation as-well-as accuracy for checks, which demands no errors because of financial concerns. IDP solutions offered by Docsumo can read and interpret cursive handwriting without compromising accuracy.
Here is a brief layout that summarizes the various distinctions between OCR and IDP:-
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A guide to automating data capture from reports, payroll or any other HR-related document into actionable format Accuracy?
In today’s dynamic business world, filing and archiving official documents in the digital form makes it handy, and works wonders in the future or in unforeseen circumstances.