Intelligent Document Processing (IDP) and Optical Character Recognition (OCR) are two confusing terms that are often used interchangeably. However, these are two different technology solutions, and IDP is considered an improvement of traditional OCR with a higher level of sophistication and maturity. So what are OCR and IDP? What is their difference? Which is the optimal choice for businesses? Find out more details in the article below.
Table of Contents
What is OCR?
OCR is the use of technology to scan characters and handwriting from a variety of image documents and convert them into computer-readable text.
OCR works by analyzing the light and dark areas that make up the letters and numbers to turn scanned images into text. Then, pattern matching and feature extraction methods are used for text recognition. After completing the processing, OCR converts the extracted text data into a digital file for use.
How OCR works. Source: statestitle.com
Some common use cases of OCR include:
- Personal identification: OCR can scan legal documents such as identity cards, passports, driver’s licenses, etc., and extract these data to store in the system.
- Extract data: OCR allows scanning invoices in PDF format and extracting information on invoices, including product name, unit price,…etc.
However, OCR also has some limitations:
- OCR works only with simple template-based documents. Therefore, when the scanned documents are not clear, or the background color and text color have no significant difference, OCR tends to have difficulty recognizing characters, leading to unexpected errors in the processing.
- OCR is not capable of handling semi-structured/unstructured documents.
- OCR cannot understand the context of extracted data, so it is not an ideal solution for businesses to scale automation.
What is IDP?
IDP is a technology that automatically collects and extracts data from semi-structured and unstructured data and converts them into structured data for use.
IDP leverages the power of Artificial Intelligence (AI) technology, including Natural Language Processing (NLP), Computer Vision, Machine Learning (ML), and OCR, to optimize identification, classification, analysis, data extraction, and data evaluation to improve accuracy and efficiency.
How IDP works. Source: mumas.in
IDP is effectively implemented in several industries to process and manage large volumes of data accurately and efficiently.
- Automatically process loan documents, deposit checks, and handwritten financial transactions: Typically, transaction forms are filled out in handwriting. IDP has the ability to improve the image quality of documents, then read and convert handwritten documents into digital documents with the help of Computer Vision and Deep Learning.
- Automatically process sales invoices, shipping documents, or insurance documents in PDF: Some documents are generated in PDF format. IDP can read and understand data files, gather information from documents, classify, extract and organize data, then combine AI technology and algorithms for further processing.
Read more: IDP Use Cases: How Businesses Across Industries Process Data More Smartly
The benefits of employing IDP technology include:
- Optimize resources: saving costs. time, effort,…
- Boost accuracy and efficiency.
- Create undisrupted workflow and seamless processing.
- Easy to integrate with an existing legacy system.
- Deliver end-to-end document processing and scale Hyperautomation.
The difference between OCR and IDP
Both IDP and OCR focus on data processing and extraction, yet they have some fundamental differences:
| OCR | IDP | |
| Data type | Simple, structured, and template-based data. | Complex data, including unstructured and semi-structured data, template-free data. |
| Capabilities | Data extraction | Analyze, classify, extract and evaluate data. |
| Core technology | Combine software and hardware. | Machine Learning core technology integrates with AI technology solutions such as Computer Vision, NLP, and Deep Learning. |
| Deployment infrastructure requirements | Complex infrastructure. | Cloud infrastructure. |
| Accuracy | Less accurate than IDP. OCR is a manual tool, so errors are inevitable in the implementation process. | Accuracy up to 99%. IDP uses ML algorithms to understand documents, maximizing accuracy over time. |
| Maturity level | Low. OCR can only perform the task of scanning and extracting data. | High. IDP has the ability to understand the contexts of complex data. |
What is better: OCR or IDP?
Both OCR and IDP can be applied in several industries to process data, documents, and forms. However, depending on the case, OCR and IDP are used to serve different purposes.
| OCR | IDP | |
| Input data | Simple. Structured, template-based documents. | Complex. Semi-structured and unstructured documents with several variations, including characters, numbers, tables, images, and handwriting. |
| Ability to process data | Can only extract data. Can work with more than 100 pages of documents per month. | Understands data in context. Can extract and process huge volumes of data, starting with 5000+ documents per month. |
| Processing speed | Slow. Manual processing causes several tasks involved. | Fast. Automatic processing. |
| Implementation difficulty | High. Requires complex infrastructure and effort during deployment. | Low. IDP can be easily deployed and integrated with the business’s big data system. |
| Deployment costs | High. OCR requires complex infrastructure that is costly to deploy, manage, and maintain. | Moderate. IDP has a reasonable implementation cost, bringing high efficiency. |
| Automation goal | Manual processes make the automation journey difficult. | Expand to a complete automation solution. |
In general, IDP outperforms traditional OCR thanks to its ability to process complex data at an excellent speed, helping solve data problems for businesses that use a large amount of semi-structured or unstructured data.
In addition, IDP can be integrated with RPA to create a comprehensive, end-to-end automation flow for the business data processing, facilitating the acceleration of the Hyperautomation journey.
Discover how IDP and RPA integration brings value to businesses here!
In Vietnam, akaBot is a pioneer in implementing IDP solutions, creating a premise for businesses’ digital transformation journey.
akaBot’s IDP solution is an ultimate choice for businesses aiming to automate data processing.
- The IDP solutions provided by akaBot allow seamless end-to-end document processing, eliminating the siloed systems with RPA, providing input data for IDP, and receiving output results to transfer to other systems.
- IDP technology developed by akaBot integrates smoothly and efficiently with the akaBot’s RPA core platform. As akaBot successfully develops IDP and RPA, businesses do not have to work with multiple vendors to implement a full automation process, thereby saving significant costs and achieving greater results.
- Fast deployment speed (4-6 weeks).
- akaBot is a “Make in Vietnam” platform with a friendly and easy-to-use interface, offering both Vietnamese and English versions for various customers.
To learn more about IDP technology solutions deployed by akaBot, contact us now to kick start your automation goal and achieve operational excellence.
References
Intelligent Document Processing 101: Your First Step Towards Digital Transformation
Understanding IDP: Data Extraction
How Can I Automate More Of My Data Extraction?
Advantages Of Using An Intelligent Document Processing Tool
akaBot (FPT) is the operation optimization solution for enterprises based on RPA (Robotic Process Automation) platform combined with Process Mining, OCR, Intelligent Document Processing, Machine Learning, Conversational AI, etc. Serving clients in 20+ countries, across 08 domains such as Banking & Finances, Retails, IT Services, Manufacturing, Logistics…, akaBot is featured by Gartner Peer Insights, G2, and ranked as Top 6 Global RPA Platform by Software Reviews. akaBot also won the prestigious Stevie Award, The Asian Banker Award 2021, etc.
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