Document processing remains a burdensome task even in highly digitized environments, because text-related data always comes in various formats that are difficult to organize. Based on our internal research and development experience, we’ll discuss practical solutions based on AI techniques that implement automated document processing in organizations across different domains.
Business Document Processing System Components
Automating paperwork with documentation involves a range of AI technologies that are used on different levels to extract information from physical documents, recognize the patterns and process text, improve data quality, and finally aggregate the data for analytical purposes.
For this purpose documentation should be searchable and compliant to the industry standards. Which is critical in various domains such as healthcare, finance, insurance, and legal departments. This is achieved through implementing data privacy regulations like GDPR, HIPAA and other industry specific standards into the pipeline.
To be more specific, the following modules perform separate tasks in a pipeline of document processing:
Optical Character recognition (OCR) – is used to scan paper documents, recognize the layout and characters to transfer them into a digital format. An optical sensor is required to provide an input for the model. Given that a single OCR model can operate within multiple cameras, it makes it easier to scale such a system unless the input parameters vary enough.
Natural Language Processing (NLP) and language generation models like GPT apply to text analysis to recognize the structure of sentences and semantic relations. NLP components can grow quite large because it will perform a number of important tasks like:
- Document classification – which is based on different parameters like layout, contained information, domain, etc.
- Data validation – is the process of validating the extracted data if it accurately refers to the original source via techniques like reference data check, pattern matching, statistical and contextual analysis.
- Layout and entity recognition – or breaking your data to the layout section and recognizing entities that are meaningful to the operation, such as client name, address, phone number, insurance number, etc.
Data quality improvement – as a process of enhancing the accuracy, completeness, consistency, and reliability of the data extracted. Some of these procedures can be handled via unsupervised learning, such as standardization and deduplication, while error handling and compliance adherence should be applied manually.
Analytics and insights – which can use any type of interface like business intelligence, separate dashboards, or simple query bar for dragging required documents from the database. Although, analytical systems can be a large module in itself, because they can entail a number of algorithms for demand or sales forecasting, predictive models, and visualization graphs.
Organizations like banks, insurance companies, real estate agencies, hospitals, and transportation companies can suffer the most from inefficient handling of paperwork. Since OCR and NLP models have become more widespread, the adoption of intelligent document processing is now accessible to mid-sized and smaller companies. The automation of paperwork itself provides a number of advantages such as:
- Administrative burden reduction;
- Streamlined business processes;
- Well-organized base of documents;
- Reduced risks and costs related to human error.
Now, let’s run through some typical examples of document types that can be processed with such systems.
What Business Documentation Can Be Automated with AI?
Documents vary in type, and generally even simple invoices will differ in their layout and entities. However, OCR paired with NLP can solve quite complex tasks given that we have enough data with decent quality. For those types of projects, we generally don’t expect issues with the training data amount, since operational documents are produced as long as the company operates and these samples can be used for training purposes.
Now that we have general representation of the automation pipeline, we also need to clarify which type of documents can be processed that way.
- Invoices used in accounts payable and invoice processing workflows.
- Purchase Orders & Receipts
- Contracts with all included entities like clauses, dates, parties involved, and contract terms.
- Shipping and Delivery Documents like waybills and packing slips, can be processed to track shipments and verify deliveries.
- Tax Forms
- Legal Documents such as case files, court documents, and legal agreements.
- Healthcare Records like patient records, insurance claims, and surgery documentation, drug recipes, etc.
- Insurance Documents as claims, policy documents, and customer inquiries.
- Real Estate Documents for instance, property records, contracts, and lease agreements.
- Utility bills
- Identity Documents such as passports, driver’s licenses, and identity cards.
We also had experience with automation of complex document types like engineering drawings and manufacturing blueprints. This type of documentation requires a custom approach to development, since there are few models that can recognize engineering patterns and extract entities that are domain specific to machinery used in 3D printing, CNC, and other aggregates. If this is something of your interest, you can read our dedicated article to learn about the techniques and methods used to process this type of documentation.
Read also:
OCR for engineering blueprintsHowever, any type of document has a similar set of techniques that allows to analyze what’s included on a paper, and extract required entities so they are correct, searchable, and structured. So let’s also discuss what data manipulations are needed to ensure smooth and secure document processing with AI.
AI Data Management Pipeline Explained
Let’s break AI-driven intelligent document processing down to several stages, and see what operations algorithms can handle either to preprocess, or improve the quality of data. Our zero step is the process of document collection from various sources like scanned documents, PDFs, emails, etc. Data-wise, documents used for business purposes are considered semi-structured or unstructured data. Which is also not consistent across sources, organizations, and also differs between document types. So the next step is data preprocessing.
Stage 1. Data preprocessing
Data preprocessing includes numerous operations that are meant to improve the quality of a document sample before it is fed to OCR and NLP models. The main operations done at this stage are:
Deskewing — which is basically a correction of angle for the scanned image, and achieving consistent angle across different scans.
Denoising — removing visible artifacts such as grain, uneven contrast, light spots, scanner defects, or any other physical and digital defects.
Binarization — converting the image to black and white.
Cropping — or framing the image.
Stage 2. Data structuring, classification, and labelling
At this stage AI algorithms will inspect, evaluate and classify data, adding labels to important fields and recognizing entities. Different fields can be combined into entities that make sense for each specific business task.
One of the most important aspects is data normalization. This is the stage when we transform the data to bring it to some common format, or change the way it is written in the database to make it easier to access. Take for instance such a common entity as an address that is important for various types of businesses. Addresses are often entered with mistakes and inconsistencies between the samples.
One sample may look like this.
- Address Line 1: Apple Park Way Building One
- Address Line 2: Floor 1
- City: Cupertino
- State: CA
- Zip code: 95014
While the other can look like that:
- Address Line 1: 1 Apple Park Wa
- Address Line 2: –
- City: Cupertino
- State: CA
- Zip code: 95014
The same address can have different variants of writing, have omitting lines, and mistakes. So the task is to match the format between different samples, fill in missing data, and remove inconsistencies. Normalized data should be easy to search or use for other purposes like forming the reports, filtering data, etc.
Duplicates and overlays in databases are another severe problem applicable to most organizations. Incorrect synchronization of information leads to difficulties with operational activity. For instance, medical, insurance, and real estate records are often recorded with miss spellings, inconsistencies, and duplicates. AI can be applied to resolve these issues, firstly training on synthetic data, and then extending to actual operational documents.
There are also cases, when the data could be partly stored in the external databases (3rd parties), not owned by the organization. Companies could create an “on the top “software layer when there’s no possibility of interfering with the data at its core. So after getting the data from its source(s), cleaning, structuring, and other operations could be used on the client’s side. That’s the list of activities where AI is applicable along with other engineering approaches.
It was the case for one of our clients, who operates in the US healthcare sector. The business faced challenges searching for similar medical cases across different national and local databases. Due to data inconsistency, it was hard to correctly identify the patient with a certain medical case. Without an identifier shared by all facilities, health institutions suffer from the violation of patient safety, data integrity failures, and medical identity thefts – the consequences of ineffective master data management strategy. We created a system that synchronizes records with proven registers, and automatically normalize patches of data mapping different entities between each other, so that the data remains searchable. Those patches of data that miss some entities are set into a separate category to be review and filled up, and further reapplied to the system to process.
Stage 3. Detecting Data Anomalies
There are plenty of ways the data could get corrupted. And in some cases, an algorithmic approach is not enough. So an AI-based algorithm for anomaly search could be performed. It allows determining data entries that match formal data criteria but are still somehow different from the data patterns in a certain dataset.
The origin of the deviation could highlight incorrect data, as well as appeal to a business process itself. So after AI evaluation, a data analytic should join to validate the results.
The process of data validation can be a separate step in overall flow. One way or another, will require involvement of a human expert to validate the extracted data and make manual corrections, since there might be domain-specific factors that impact quality of data.
Stage 4. Getting Insights from Data Analytics
As a final destination for our data, once normalized and validated documents are ingested into the database, they need to be accessible to the right parties in suitable format. This brakes the analytical component into multiple important aspects:
Integration with databases — as a part of engineering tasks to settle the connection between different databases and end-user interfaces.
Access policies — are required in case analytical software is provided to employees of different levels, to set the limitations for accessing required data layers.
Analytical functionality — which can range from simply querying that returns excel to the user, to customizable dashboards that source data in real time and turn them into visualizations, or automatically compile data into reports on a weekly/monthly basis.
Analytical software is usually a separate project under the digitization/document processing automation umbrella. However, if you aim to bring maximum value with the automation of paperwork, aggregating data to make it more accessible is a logical step. Since in most cases, the absence of a user interface that presents the insights to required parties will lead to lower ROI for such projects.
However, there are alternative solutions that are based on NLP capabilities that allow an end user to enter queries in spoken language to search through data. This is what’s called an NLP recommendation system and can be applied to different tasks that require a search system to understand the context of human speech and tolerate inaccurate sentences. You can watch our short video that shows a demo on NLP-based search engines and how they work in practice.
Why Develop a Custom Intelligent Document Processing System?
The market of automation technologies is full of different solutions tailored to document processing as well. However, it is important to understand that vendors who propose platforms for document processing as a SAAS solution, may have a lot of restrictions such as formats and amount of data they can accurately handle, security concerns, and scalability issues.
If you deal with large amounts of paperwork that needs to be managed digitally, and a large amount of these documents are unique, it’s better to go with a custom solution. Since NLP and OCR modules trained on your specific data will do a better job at extracting valid information, while maintaining better control over your general dataflow.
Another important aspect is that custom solutions can be integrated with other software components more seamlessly, offering a robust and secure ecosystem for data transmission. MobiDev has years of experience in developing AI applications integrated into corporate workflows. Feel free to set up a call with us to discuss project details.
