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Data extraction is a crucial part of document processing that allows businesses to extract valuable information from their documents quickly and efficiently. In this article, we quickly define automated data extraction for document processing. Following which, we discuss different document types, and different data extraction components.
Most importantly, we provide a step-by-step guide for businesses to choose an automated document processing software by discussing:-
i) Scale of the problem,
ii) Need for automated document classification
iii) Required accuracy metric,
iv) Need for customization
v) Cost and ROI of the project.
By the end of this article, readers will have a comprehensive understanding of automated data extraction and be able to make informed decisions about which approach is best for their specific needs.
So, let’s jump right into it:-
What is data extraction?
Data extraction can be defined as the process of transforming unstructured or semi-structured data into structured information. This structured information provides companies with meaningful insights to be available for reporting and analytics.
Automated data extraction is the process of extracting data from unstructured or semi-structured data without manual intervention. It is a pipeline with components like Data preprocessing, Data extraction, and Data validation. The higher the accuracy of the Data extraction component, the higher the automation.
Data extraction from documents for automated document processing
This is how structured data looks like in table form:-
However, systems often have to deal with long, textual data made of long strings of typed characters. These documents may contain images, videos, spreadsheets, audio files, and other multimedia content. This data is collectively referred to as unstructured data because it had no fixed format.
When we look at documents from this lens, all documents collectively can be categorized into the unstructured data category.
This is the first point of confusion - unstructured and structured data do not map to structured and unstructured documents.
All documents are unstructured data. But within these documents, we can further classify them into three categories based on how they appear:
1. Structured documents
Structured documents are characterized by a set of information where the formatting, number, and layout are consistent from one document instance to another. These documents are also referred to as fixed forms, and some examples of such documents include W2 forms, W9 forms, Acord forms, payment slips, utility bills, driver’s licenses, and passports.
2. Semi-structured documents
Semi-structured Documents have a fixed set of data but no fixed format for this data. In some documents, the date appears in the top right corner, in another variation, it is at the center of the document, and in yet another, you’ll find it in the bottom left corner. Another added complication is that different names qualify the same data.
In one variation, a field may be called ‘Purchase Order Number’, in another - ‘PO Number’, and a few others may call it “PO #”, “PO No.” or “Order Number”. Examples of semi-structured documents include invoices, rent rolls, and financial statements.
3. Unstructured documents
Unstructured documents, as the name suggests, contain information that is presented in a free format without any specific layout or organization of content. Extracting information from unstructured documents can be challenging since the data is not organized in any specific way or separated into specific sections or fields. Examples of unstructured documents include emails, letters, contracts, and reports.
Data extraction solutions
After understanding the different document types, it's important to know how companies extract data from them. Automated Data Extraction tools use either rule-based or AI/ML-based solutions for data extraction components. Let’s learn about these solutions:-
1. Rule-based Data Extraction
The method first uses Optical Character Recognition (OCR) to convert images of text into machine-readable text. The OCR information is passed to the next steps of the pipeline. The next steps use hard-coded rules and workflows varying for each document type. Both image and text-based patterns in each document type are used to write custom rules.
These rules need to be updated even for minor changes to the structure. This solution can’t deal with the variety and complexity of documents from diverse sources, and it struggles to provide consistency in the process.
For example, a company that processes a large number of invoices from a single vendor can use a rule-based extraction solution to automate the data extraction process.
2. AI/ML-based Data Extraction
This method also first uses Optical Character Recognition (OCR). Along with the text information, layout and style information are vital for document image understanding. Today with the advancement of Artificial Intelligence, more specifically the innovation of MultiModal learning for data extraction, AI/ML-based document processing has State of the Art (SOTA) results for data extraction from semi-structured and unstructured documents.
AI/ML Based extraction has made significant progress in the document AI area. This solution can extract data from documents with varying content and structure. It can deal with the variety and complexity of documents from diverse sources. Further, It can adapt to changing structures by finetuning or pretraining the model on the updated data structure.
A step-by-step process to choose automated document processing software for your business
Straight Through Processing (STP) can be defined as the percentage of documents processed/extracted without needing any manual human correction.
The accuracy of the data extraction solution is directly proportional to the STP of automated data extraction. So it’s important to select a data extraction solution that provides high accuracy for your use case. Calm your horses, Have you defined your use case and its scale? Look for these factors while choosing the document processing solution for your business:-
Step 1 - Define the scale of the problem
The first step is to define the scale of the problem, which includes the number of documents that need to be processed, the type of documents, the type of data that needs to be extracted, and the minimum STP needed. It is essential to have a clear idea of the scope of the project before proceeding to the next step.
Figure out how many documents the business needs to process
Today with the rise in computation power and cheap storage, the number of documents is not a roadblock on your automation path. Many document ai companies have architecture designed to scale as the number of documents increases. You should look for Document AI companies that offer secure storage and backup options.
Figure out type of documents to process
Your choice of solution for the data extraction component depends on whether the documents to extract are structured, semi-structured, or unstructured.
Rules/template-based extraction provides perfect STP for structured documents. E.g. In W9 Forms, The fields to be extracted, such as name, address, and tax ID number, are always in the same location on the form. A rule-based extraction solution can be easily set up to extract these fields accurately and efficiently.
But it is not a reliable data extraction solution for semi-structured and unstructured documents because different formats of document type need different rules. Furthermore, these rules need to be updated even for minor changes to the structure. The documents may come from third-party sources, so their format is out of our organization’s control. Hence, they can be very diverse.
E.g. A lending company receives bank statements from its applicants, but the statements vary in format and structure. In this case, a rule-based extraction solution would not be reliable because different bank statements would require different rules, and the rules would need to be constantly updated as the statements change.
As discussed earlier, AI/Ml-based extraction can deal with the variety and complexity of documents from diverse sources. Further, It can adapt to changing structures by finetuning or pretraining the model on the updated data structure. Hence, it can be a reliable data extraction solution for semi-structured and unstructured documents.
E.g. In the above example of bank statements, an AI/ML-based extraction can adapt to the varying formats and structures of the documents. The system can be trained on a large number of bank statement examples to accurately extract the necessary information, even if the statement format changes.
Structured, Semi-structured, and unstructured are generic classifications of documents. Each of these generic classes has some real-world document types like Fixed Forms (Structured), Invoices (Semi-structured), and Contracts (Unstructured). Understanding your specific document type will later help you decide whether you need a custom solution.
Figure out data points to be extracted
The data to be extracted from documents can be categorized into three high-level groups. They are:-
1. Key-value pair
Key-value pair is a type of data extracted from documents. A key-value pair consists of two related data elements: a key, which is a constant that defines the data set (e.g., Invoice number, Seller address, Total amount), and a value, which is a variable that belongs to the set (e.g. INXXXT65532, 240 Washington St, Boston, MA 02108, United States, $68637). Fully formed, a key-value pair could look like these:
Invoice number = INXXXT65532
Seller address = 240 Washington St, Boston, MA 02108, United States
Total amount = $68637
Checkbox extraction from documents is the process of identifying and extracting the status (checked or unchecked) of checkboxes present in a document. Checkbox extraction is particularly useful in scenarios where we need to extract data from forms that have been filled out with checkboxes, such as Tax forms like Form 990 and 1040 family.
An example of the use of checkbox extraction can be seen in Form 990. Part VI of Form 990 requires organizations to disclose information about their governance, including whether they have certain policies in place. This information is presented as a series of checkboxes, which the organization must mark if the policy is in place. Checkbox extraction can be used to automatically extract this information from Form 990.
Generally, a table provides a useful structural representation that organizes data into rows and columns and aims to capture the relationships between different elements and attributes in the data. Another type of table is nested tables, which are generally present in documents like rent-roll and are hard to extract.
Tables also vary in a layout based on the type of documents. Financial statements, rent rolls, and invoices all have different layouts present.
Both rule-based and AI/ML-based solutions have different architectures depending on KV pair extraction, Checkbox (OMR) extraction, or Table extraction.
Step 2 - Automatic classification of document types
If you have multiple document types which are not organized by folder, then the ability of the Document AI platform to classify documents to their respective document types is an important feature.
Step 3 - Check for STP metric of the solution
We have already talked about STP at the beginning of this section. But remember STP rate is a critical metric to consider when choosing a data extraction solution.
Step 4 - Decide if you need a custom data extraction solution
Document AI companies have generic solutions for different document types. For structured documents (W2 forms, W9 forms, 1040, and others), they may have either rule-based or AI/ML-based generic solutions. For these document types, you will not require a custom solution.
But for semi-structured documents (Bank Statements, Invoices, Financial Statements, Rent rolls, etc), they have generic AI models. For each document type, models are trained on a huge volume of data consisting of varying content and structure. You can simply use those models as they provide high STP. You will need a custom solution for below reasons:
1. The document type is unique and the document AI companies don’t have a generic model. In that case, the Document AI company will train a model on the (50-100) documents provided by you. They will use techniques like Data augmentation (increase training data) and transfer learning (utilize a similar doctype’s generic model) to provide STP that meets your needs.
2. The generic model doesn’t provide the STP you need. In that case, the Document AI company will finetune the generic model on the (50-100) documents provided by you.
Step 5 - Analyze the cost and ROI of the project
The cost of the project is dependent on various factors such as the volume of documents to be processed, the complexity of the data extraction, and the level of automation required.
It is important to note that the cost of the project should be weighed against the return on investment (ROI) that the project will bring. For example, if the manual data entry process takes a lot of time and resources, automating the process will save a significant amount of time and resources in the long run. This could result in a positive ROI for the project.
In addition to the ROI, businesses should also consider the long-term benefits of automated document processing, such as increased efficiency, improved accuracy, and faster processing times. These benefits could translate into better customer service, increased productivity, and improved decision-making.
When evaluating the cost and ROI of the project, businesses should consider the following factors:
1. Initial setup cost - This includes the cost of software, hardware, and any necessary infrastructure changes.
2. Ongoing cost - This includes the cost of maintenance, upgrades, and ongoing support.
3. Processing cost - This includes the cost of processing each document. This cost is dependent on the complexity of the document and the level of automation required.
4. ROI - This includes the benefits of the project such as increased efficiency, improved accuracy, and faster processing times.
By carefully considering the cost and ROI of the project, businesses can make informed decisions about whether to pursue automated data extraction and what type of solution to implement.
Finally, quick checklist to choose a document processing software
There are several features that one should make sure, their Document Processing Software possesses:
1. Categorical identification
Tax returns and HR applications cannot be put on the same page. Hence, we should develop a software that should be able to collect and manage data from different forms of documents like delivery notes, contracts, applications, and whatnot.
2. Flexibility in formats
Different companies have different requirements for file formats for performing several tasks. The software should have the capability to scan information across several most used standard file formats.
3. Speed and accuracy
The faster the speed, the more preferred is the solution. But the high precision of numbers and high data accuracy is a must, independent of any sector you work in.
With the advancements in technology, we are well equipped with intelligent OCR that automatically extracts data using neural networks & reverse image search. Such AI-based systems learn themselves which leaves a negligible chance of error as well as an easy extraction process.
5. No need for third-party applications
Third-party applications come handy with the high cost and greater reliability on it. Another issue that exists is that it is not customizable according to the changes by the client. Thus, the reliability of third party applications is not a preferred choice.
6. Storage and Backup
Cloud supports the best backup option due to its scalability, accessibility, and security. It can be termed as a digital backup for regulatory purposes of any company which aims to speed up the audit during a crucial year ending period.
The document processing software should be well equipped with some highly protected permission settings. This should ensure only the appropriate access is given to the sensitive information. Also, there should be infrastructure monitoring tools to have a quick look at who can access what to prohibit privacy breaches.
Sending invoice data in one click to any other software using the API can facilitate the ease of using a document processing system. Different APIs consider different formats as per need like some may accept JSON or only XML. Thus it is important to note that the extraction of data in every format should be simple yet efficient to integrate or consume by the client for different purposes.
9. Capture data intelligently
The right software captures data from a variety of input devices. After getting the document scanned, the system should undergo an intelligent extraction process and should be well equipped to validate the data in the files. The next step involves tagging and categorizing documents for fast retrieval. This is best done through an easy-to-use search engine, that aims to conserve your staff's time and adds to the overall productivity.
Automating data extraction can bring significant benefits to businesses, including increased efficiency, improved accuracy, and faster processing times. To implement an effective solution, businesses should define the scale of the problem, consider the need for customization, and evaluate the cost and ROI of the project. By carefully considering these factors, businesses can select the right data extraction technique that suits their specific needs and achieve maximum benefits from automation.
We at Docsumo ensure that we serve you with high accuracy, high speed, and work with documents without manual setup. Also, it joins with amazing features like auto validation to perform most of the checks to validate using the AI engine. If you prefer the smarter way to process documents, signup for a free 14-day trial.
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