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:-
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.
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.
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.
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.
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:-
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.
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.
The traditional supply chain management approach relies heavily on manual work and is time-consuming, error-prone, and expensive. As documentation is an important part of the supply chain that consumes considerable efforts of enterprises in the supply chain workflow, it makes sense to automate the process with the help of intelligent document AI software.
Optical Character Recognition (OCR) is the technology to convert an image of text into machine-readable text. It is the underlying technology for various data extraction solutions including Intelligent Document Processing. However, OCR is not smart enough to figure out the context in a document - it works simply by distinguishing text pixels from the background and finding a pattern. This limitation could cause inaccuracy in captured data that could directly impact the output of your data extraction model.
Accounts payable is a key financial function for any business. Corporations can have thousands of suppliers; even for relatively smaller businesses, the number of suppliers could be in hundreds. All the invoices they receive from these suppliers come in multiple formats, layouts, and templates - some semi-structured, some unstructured. Therefore, firms expend time and resources to capture invoice information through manual data entry and verification of accounts payable. Manual data entry is not feasible in the long run, definitely not on a large scale. Before we talk about how intelligent invoicing solves the problems associated with manual invoicing, let’s discuss the challenges in much detail.