Automatic document classification leverages AI technologies to automatically recognize and categorize documents, making them easi
Document management remains a vital aspect of modern businesses. Yet, many businesses struggle with this task, especially given that 80 percent of business information is still paper-based. Sorting through thousands or millions of documents manually can be an arduous and time-consuming task. As a result of these challenges, businesses experience a 21.3 percent productivity loss.
It’s no surprise, then, that automatic document classification has become a must-have tool for businesses to manage their documents efficiently. With the advent of machine learning, organizations can now optimize document classification even further. In this guide, we’ll explore what document classification is, how it works, and the ways machine learning can power up your document management process.
Document classification is the process of sorting documents into categories based on their content, format, or other characteristics. It is a branch of Intelligent Document Processing (IDP) that efficiently categorizes documents into pre-defined classes. This helps businesses organize and group documents together for easy retrieval and processing. With document classification, it's easier for machines and people alike to process and digitize them quickly. After they are classified and processed, managing, searching, filtering, or analyzing the data embedded in these documents becomes simpler than ever before. Moreover, classifying documents allows IDP solution to further decide which data extraction models, or sometimes known as extractors, to use to extract specific data fields.
For instance, think of the myriad invoices an organization can receive, such as reimbursements, bills for office expenses, and third-party software. Generally speaking, these need to be sorted into different folders or databases depending on the invoice type. However, if you attempted to categorize hundreds of thousands of invoices manually, it would be quite improbable to achieve absolute accuracy. Moreover, manually examining all these documents one by one is laborious and tedious work. Thus, automated document classification can be extremely useful for cases such as this one.
Structured documents or fixed forms provide a consistent and organized format for presenting information. The font, numbering, and layout are standardized and do not vary. These features make it straightforward to develop automated solutions to convert these physical structured documents into structured data, as their predictability and consistency facilitate the processing of information. Some good examples of structured documents are ID cards and business registration certificates.
An unstructured document typically contains information whose format is open. Examples include contracts, orders, letters, and bills. Tables within these documents may not contain borders, making it much more challenging for a document classification algorithm to determine their exact position. Identifying the required data is thus more difficult.
Semi-structured documents are a unique blend of structured and unstructured components. Multiple static tables with data portrayed in various formats often occupy the same page, creating an entirely new document type.
In the business realm, invoices and receipts are among some of the most commonplace semi-structured documents. Gleaning information from these particular documents can be a complicated task due to their blend of structured and unstructured components.
Document classification may be divided into two broad categories: manual document classification and automatic document classification. Manual document classification requires a person to go through each document and decide which category it belongs in. As you have to manually analyze the text and other components to determine the connections between concepts and sort documents, handling a vast number of papers is immensely difficult and likely to cause mistakes.
Conversely, automatic document classification allows documents to be classified into categories without the need for human intervention. This is made possible with machine learning models, which can be trained to make sense of the document’s context and classify it accordingly. With machine learning, the process of document classification becomes more accurate and efficient than manual methods. The process is faster, more cost-effective, and more scalable.
Before being able to classify documents, the textual information must first be digitized, which can be another laborious process if done manually. Many solutions then utilize Opitcal Character Recognition (OCR), and other AI technologies like Machine Learning (ML) and Natural Language Processing (NLP) to automate document digitization. Afterwards, they will be ready to be classified.
Automated document classification using machine learning is a sophisticated process that involves three main steps:
Let's take a closer look at each of these document classification steps.
You must first collect enough number of samples to train the machine learning models. By familiarizing with the layouts and content of these samples, the document classification engine can then build up different classifiers that will be used to assign different tags or categories to documents.
Gathering data from documents is only the beginning. Now, you need to test the document classification engine to see whether it accurately sorts the documents into the correct categories.
After testing the accuracy, the solution can then be integrated with existing software or application where images or documents are uploaded and sent to the document classification engine for sorting. By sending mroe samples to the IDP solution, it will continue to learn and gain a deeper understanding of your document categories.
It's hardly possible to overemphasize the importance of automated document classification and processing for businesses. By automating the sorting and processing of documents, businesses get to:
As businesses become increasingly inundated with data, it is becoming a challenge to keep track of all vital operations. Take the example of an operations manager at a hospital who has to manage and oversee documents within the facility. These include appointment confirmations, doctor's orders, medication information, financial records, and future appointments.
With hundreds of patients, file organization could be managed by manually storing these documents in different folders. But when the number of such folders reaches thousands, it becomes impossible to manage this data manually. Yet the information contained in these files is crucial for making crucial business decisions. To tackle this issue efficiently and effectively, companies should contemplate applying document classification and processing technology as an ideal solution.
Automated document classification capabilities can help businesses protect sensitive or confidential data by ensuring that the correct security rules are applied as documents move through different systems. By exploiting automated classification, business information is automatically organized, allowing for easier protection against unauthorized access and human errors. Furthermore, document classification helps businesses ensure compliance with various types of regulations, such as GDPR.
Using document classification engine powered by machine learning can significantly streamline the process of sorting and extracting information from numerous documents. The right approach for your business depends on the unique needs, requirements, and size of your document collection. By evaluating these factors, you can identify which type of system is best suited to your organization's needs. With a document classification and data extraction system like FormX in place, you'll be able to classify massive amounts of textual data quickly, efficiently, and accurately.
Whether you have a single or several documents in an image, the Detect Documents feature of FormX will be able to identify the type of each document and return the coordinates of the bounding box for you to split them for data extraction or further processing.
When testing, you can also verify the JSON output and integrate FormX with your software or application via API.
Get in touch with us today to find out how FormX can help you streamline and automate your operations. With our automated processing solutions, you'll be able to tackle even the most complex data processing challenges in no time.
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