‘Parse’ originates from the French word for ‘to divide up’ or ‘analyse’. Parsing means dividing a document into relevant data and restructuring them into a uniform whole for analysis.
Alternatively, a more formal definition for document parsing is extracting unstructured data and converting it into a structured format. You can conduct document parsing via specialised technology that ‘reads’ the data and organises it into a predetermined format. You may have also heard data parsing referred to as data extraction or data capture.
For businesses handling consistently large volumes of documents, document parsing represents a time-saving data management strategy. Instead of manually poring over long documents to find specific data points such as dates or names, document parsing software automatically locates and structures the required data.
Lenders dealing with floods of invoices or alternative financiers receiving100+ page annual reports represent just a few use cases for automated document parsing.
The first step to understanding how you could apply document parsing software to your workflow is knowing how the technology operates.
A fascinating comparative study of PDF parsing methods states that there are two broad approaches to document parsing: rule-based and learning-based. Accordingly, there are two variants of document processing software: Optical Character Recognition (OCR) and AI.
OCR is exactly how it sounds – a technology that recognises and interprets the shape of characters like letters and symbols. The technology matches document information with its library of characters to extract the data and organise it according to predefined rules.
The main problem with OCR-based document parsing is that it’s inflexible due to its prescriptive paradigm. Change the font or structure, add handwriting or an image, and OCR fails. In other words, it doesn’t understand the data – only what it looks like.
AI-based technologies like Natural Language Processing (NLP) enable document parsers to ‘read’ documents like a human – understanding and selecting between the potential meanings of words. By examining the context of words with multiple meanings, like ‘check’ or ‘book’, AI-powered document parsers can select the required data more accurately than OCR and structure it in more helpful ways (such as calculating financial ratios).
As AI falls under the umbrella of a ‘learning-based’ parsing approach, it learns from its mistakes, never repeating them. In contrast, OCR requires significant training and updates to adapt its rules.
Ultimately, OCR and AI aren’t mutually exclusive document parsing technologies. Rather, AI is designed to circumvent OCR’s limitations. All of the methods of document parsing that we’ll describe use OCR technology – yet also deploy AI-powered algorithms that can validate OCR’s output.
The main advantage of OCR is that it is more of a ‘household name’ when it comes to back-office technology, meaning it might be easier to find an industry-specific solution and win over stakeholders. However, OCR’s inherent inflexibility also means the future of document parsing (firmly) belongs to AI.
Another way to interpret document parsing is by converting files from one type (e.g. a PDF or image scan) into another (e.g. a JSON file). The difference between document parsing and file conversion is that document parsing is selective about the data it parses, compared to file conversion, which transcribes all the parsed data into a different file format.
PDFs are a common way to store information, as they contain data about how to structure lines and characters. The problem with PDFs is that they often contain irrelevant information which cannot easily be edited out of the document (compared to a file format like Excel, where it’s just a matter of pressing the ‘Delete’ key).
Of course, manually editing the PDFs won’t be overly time-consuming for small businesses. However, enterprises will receive mass quantities of PDFs, such as:
The data must be extracted and structured quickly from these PDFs to provide value. Therefore, enterprises may establish workflows or tech stacks incorporating document parsing technology. From the moment the PDF is available, its data is automatically read, extracted, cleaned, validated and structured before being actioned.
JavaScript Object Notation (JSON) parsing involves converting a JSON string into a structured data format that can be easily understood and actioned by a programming language. It has a straightforward syntax and is a useful communication tool between programs. For example, if you were building an app, JSON parsing could fetch real-time data and display it on the screen.
For technical audiences, The Guardian wrote an article exploring the benefits of parsing JSONs. For non-technical audiences, JSON parsing might not be a suitable use case.
There are several ways to access document parsing tools, depending on your technical expertise and available resources. Let’s explore four of them.
If you’re a technology developer (or have strong Python skills), you can build your document tool in Python. Packet Coders has a practical article illustrating how to code a document parsing tool. The main drawback of this issue is that embedding scalability into a self-made model can be tricky. Here’s why – it requires an expert approach to data engineering and significant computational power.
You can use commercially available large language models (LLMs) like ChatGPT, Anthropic’s Claude and Google’s Gemini to parse data. If using their interfaces directly, the experience may be somewhat clunky, as you will need to copy and paste the output from the widget they provide (as of November 2024, these LLMs no longer offer a ‘download’ option…). Make it clear what format you want the data to be parsed in (e.g. CSV). Otherwise, the data will be outputted as a generic list of data points.
When we’ve written extensively about extracting data using ChatGPT, we also addressed the tool’s convenience. You can experiment yourself by uploading a document and trying out different prompts.
Application Programming Interfaces (APIs) are connective tools commonly used in programming.
One key advantage of document parsing APIs is that they are configurable, meaning you can add and remove your custom rules for extracting and structuring data. You can either build one yourself (which is likely to take a while and require significant technical expertise to build the front end and back end, integrate machine learning technology, etc.) or use a vendor-provided API.
For enterprise document parsing or parsing in bulk, you might consider using a reputable third-party vendor to parse your documents securely and return the data. The advantage of using a vendor is that they can customise the parsing API according to your requirements.
However, the main disadvantage of using a vendor is choosing the right one out of the dozens on the market. Such vendors will showcase different specialities – some are custom-built for resumes (e.g. HireAbility), others for invoice data parsing, etc.
AI excels at identifying tiny errors. For example, this year, MIT researchers have invested resources into building error detection mechanisms designed to pinpoint anomalies in equipment maintenance. Yet, it would be overly idealistic to claim that document parsing tools are infallible.
In certain instances, document parsing tools might fail, outputting data with decreased accuracy or slowing down its parsing rate. Let’s examine two examples of these instances.
The best way to test integration is by measuring its response to a surge of documents. When large volumes of documents are uploaded, the demand for system resources like CPU and memory may cause document parsing web applications to fail, freeze or release inaccurate data.
Well-integrated document parsing tools will be better able to overcome performance bottlenecks. For instance, APIs are designed to bridge the data flow between programs, ensuring that more data doesn’t equal low-quality performance.
Document parsing tools may struggle to maintain performance when confronted with large, complex tables, such as those with thousands of rows and columns. The solution? Splitting tables into smaller components. Though splitting the document may take extra time, it may save time if it reduces the need for manual review.
To avoid investing time and resources in a document parser that doesn't work well with complex data or high volumes, considering running a Proof of Concept (PoC) or test.
.png)
Currently, document parsing software offers high accuracy (typically at around 99%). Zero-shot learning – an AI technique – also offers high accuracy on unseen document types. Furthermore, we may begin to see more document parsing software adopt zero-shot learning, delivering ~100% accuracy on documents without training the software.
If AI-powered document parsing software can ‘read’ documents, it can also perform simple data analyses. For example, parsing a financial statement and structuring the required data requires an intrinsic understanding of the relationships between the financial information on the page. Case in point – understanding that ‘profit’ could also be expressed as ‘earnings’ or ‘net income’ and that trademarks are non-tangible assets.
Document parsing is an important data management function. Well-established document parsing technologies can ‘read’ documents, correctly extracting and structuring the information in seconds. Linking a parsing tool to an internal database can allow data to flow smoothly through an enterprise system, providing value through its accessibility.
In the last few years, document parsing technology has evolved considerably. Aside from performance improvements, in the future, we might see document parsing software with further generative capabilities, such as creating videos from documents and more.
Evolution AI’s technology parses financial documents expertly, capturing data in the required format. Our products are powered by academic and industrial expertise, ensuring we can deliver the best possible performance at the lowest cost. Learn more about our services at hello@evolution.ai or book a demo with our financial data project team.
