Substantial Growth with Automated Document Processing
October 21, 2022
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7 min
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Automated document processing involves capturing components present on a document with the help of softwares. It utilizes technologies like Machine Learning, Computer Vision, Natural Language Processing, and OCR. Automatic processing of documents in an organization helps reduce manual labor, compliance requirements, eliminate challenges, and offers speed to the workflow environment. 

In this article, we cover different techniques used for document processing along with their pros and cons. This comparison will help you choose the best automated document processing software for your organization. 

There are four common ways to process documents in an organizational setting:-

1. Manual document processing

Manual document processing refers to processing relevant and important information from documents manually and arranging this data in a decision-driving manner. This technique is a time-consuming process which can take up to 20 minutes (sometimes more) to process a single document. When it comes to the accuracy of manual data processing, it is comparatively low to other data processing techniques available with only 60-70 percent accuracy. This method also requires a manual workforce to carry out the whole operation.  

2. Computer Vision

Computer vision pertains to training the computer with a series of document formats to provide the capability of identifying characters and other data-driven elements from a document. It is a modern data processing technique that uses artificial intelligence to derive meaningful data from images, videos, documents, or anything that holds digital or analog existence. This can be better explained as artificial intelligence that can make the computer think. It helps see objects, draw observations, and then understand. Computer vision drives other methods like Optical Mark Recognition and Optical Character Recognition, and is the superset of these data processing techniques.

This process involves using a lot of data in repeated analysis until it recognizes the distinctions and data from the images or documents. To understand the functionality of computer vision, let's take the example of resumes. In order to train a computer on the difference of resumes for recognition, you need to feed large quantities of resume documents to learn and understand the differences for a distinct recognition.

Computer vision utilizes two different technologies, namely deep learning and CNN, to accomplish distinct recognition. CNN refers to convolution neural networks that assists the machine learning model to look at images broken down into pixels with labels or tags to perform convolutions and make predictions.

3. Optical Character Recognition

Optical Character Recognition or OCR identifies data from documents in the form of characters and images and further processes this data into accountable formats. This extracted data is then converted into a machine-readable form, further used for data processing. OCR processes digital files like employment receipts, invoices, contracts, financial statements, etc. 

Optical Character Recognition helps automate document processing and data extraction, which eventually leads organizations to save precious resources and time. This technology analyzes text present on a page, identifies characters, and further turns them into a code that supports information processing in the document. It has a three-step procedure that includes pre-processing, character recognition, and post-processing.

4. Intelligent Document Processing

IDP stands for Intelligent Document Processing, which transforms semi-structured or unstructured information from a document into usable data. Approximately 80% of all organizations' data is stored in semi-structured and unstructured form like invoices, profit & loss statements, and balance sheets. Intelligent Document Processing has brought revolutionary changes to the next generation of data processing automation with extremely fast processing and capabilities like extraction and processing from various document formats. 

The automated document management system utilizes AI technologies like Natural Language Processing, Deep Learning, Computer Vision, and Machine Learning to classify, categorize, and extract relevant and important information, eventually validating data. IDP is the next step of Optical Character recognition as it overcomes OCR limitations in data extraction from all non-standard and complex documents. It has a high accuracy of close to 100% and has quicker functionality than other data extraction methods with the ability to process data from complex document structures.

How to automate workflow using automated document processing techniques 

The two most popular techniques, OCR and IDP, facilitate automated workflow. Here is a quick comparison of the pros and cons of different document processing techniques:-

Features Manual Optical Character Recognition Intelligent Document Processing
Turn-around time Approximately 15 minutes or more 2-5 minutes per document page 30 seconds to 1 minute per document page
Accuracy 70-80 percent 80-90 percent Close to 100 percent
Human Intervention 100 percent Required for data processing Only for data validation
Data Interpretation Yes, can be done No, can't be done Yes, can be done
Self Learning Ability Yes, can be done No, can't be done Yes, can be done
Different layouts formation and complex documents processing ability Yes, can be done No, can't be done Yes, can be done

OCR or IDP? 

IDP vs OCR

Optical Character Recognition and Intelligent Document Processing are two different, yet overlapping technologies used for the same purpose. However, Optical Character Recognition is a cheap version that offers comparatively less accurate and slow data processing from different document formats. Intelligent Document Processing utilizes a powerful combination of Optical Character Recognition to convert text into machine-readable language using advanced and intelligent AI technology to perform several operations. The accuracy offered by IDP is close to 100%, while for OCR, it is 80-90%. If we talk about data interpretation, it can be done with the help of IDP but not with OCR. 

Intelligent Document Processing can capture documents correctly in a shorter span of time if compared to OCR and enables further classification and data extraction to automate your workflow with enhanced efficiency and effectiveness.

Why is IDP the winner?

IDP refers to Intelligent Document Processing, and is the most accurate and efficient digital document processing technique capable of processing a large set of document formats. It identifies data from any written document and performs further identification of the sets of useful and relevant information for further use. 

Intelligent Document Processing, as mentioned above, functions using a combination of artificial technologies and Optical Character Recognition to process several document formats, thus offering you a large variety of accessible documents. By promoting automation, it eliminates the processing of data manually, and modifies the workflow to automate several functions. This can include capturing information, data entry, sorting documents, conversion, indexing, and routing company records. The outcome of automating these tasks can result in the simplification of compliance because IDP leaves behind a cyber trail for audits and to comply with elaborate regulations.

IDP can successfully process unstructured, semi-structured, and complex documents that organizations and businesses function on regularly. Data privacy issues are no longer a problem when IDP is used since it employs secure technology that is capable of preventing the manipulation or misuse of data. Not only does adopting Intelligent Document Processing reduce the time it takes to process documents, but it also cuts down significantly on other costs, including labour. 

The limitations of IDP are not limited to data capturing and recognition. It also performs successful data extraction from these documents, enabling a quick workflow without any risk of errors that eliminates the time consumption in further detailing. 

Here is a smart case use of IDP with an example to help you understand how it is a better performing asset for organizations that deal with loads of documents processed data.

The best use case of IDP is the ability to interpret context from the text using artificial intelligence. It understands the similar context of $500 and five hundred dollars. IDP performs data processing in a way manual processing can never achieve, including collecting, analyzing, and extracting data from several documents. 

Benefits of IDP

Benefits of IDP

Intelligent Document Processing bears several benefits to your organization. A few of them are:-

  • Enabling end-to-end processing of documents from capturing to extraction. 
  • It eliminates the manual need for data typing and extraction, relieving you from hours of work that can be utilized at better places. 
  • There are close to no chances of errors when it comes to IDP. 
  • Enhanced rapid workflow with reduced cost to the company with savings up to 70%
  • It offers scalability that essentially means high organizational flexibility. 

Why choose Docsumo for automated document processing?

Docsumo stands out with its targeted document processing. Intelligent Document Processing consumes Artificial Intelligence blended with Machine Learning and Deep Learning operations. End-to-end document processing for many document types is ensured — from unstructured to complex structured documents. We offer low-cost company workflow solutions with advanced and top-notch technology. 

Schedule a demo today to see Docsumo in action.

Written by
Pankaj Tripathi
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Substantial Growth with Automated Document Processing
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