In March 2025, Mistral AI announced that they were releasing a new OCR tool for developers that converted PDFs into raw text. Optical Character Recognition (OCR) is a technology designed to do exactly that: change images into machine-readable text. It’s a versatile technology that’s most often deployed to convert PDFs into an actionable format.
Invoice data capture software is a technology designed to extract relevant data points from invoices. Common examples of these data points include:
OCR invoice capture software converts these data points into the desired format - such as an Excel file. OCR invoice capture software should make collecting data quicker, more accurate and less costly. Ideally, OCR should make manual data entry obsolete, eliminating invoice processing mistakes caused by human error.
OCR data extraction technology converts unstructured data from a scan or PDF of an invoice into searchable, machine-readable text. For example, if you input a batch of invoices, OCR data capture software would release a structured file containing VAT totals ready for downstream processing.
OCR invoice capture software can (at least theoretically) also extract handwriting, such as signatures or notes on an invoice, and convert it into raw, machine-readable text.
In the 1920s and ‘30s, Emmanuel Goldberg developed an early version of OCR that used an optical recognition system to trawl microfilm archives. While OCR was the first data capture technology, is it still the best out there?
For enterprises, OCR for invoices will likely lead to a disappointing experience. Invoices are semi-structured documents - while they conform to a general structure, there are variations and multiple optional elements. There is no such thing as a typical invoice, meaning it is extremely difficult to train an OCR engine to locate the pertinent data points across all invoices. In general, OCR’s most effective use cases rely on clearly defined templates, such as number plates and cheques.
Ultimately, does OCR-based invoice capture software work satisfactorily for invoices? The answer is not really – at least, not without a significant degree of training and correction. In the interest of fairness, however, you will likely experience success with OCR if you’re working with a high volume of invoices with completely static layouts.
One of our founders, Dr. Martin Goodson, described his experience working with traditional OCR technology:
“We founded a startup for technology to automate tax calculations for self-assessment to make tax returns easier. The OCR technology we used wouldn’t read usage slips and kept breaking. We were shocked – previously, we assumed that OCR just worked. I had no idea it was so primitive. So the startup failed.”
Motivated by the frustration that plagued their previous project, our founders created Evolution AI. Many other tech entrepreneurs have faced similar experiences, creating a range of alternatives to OCR currently on the market. As for the best alternatives to OCR available commercially? AI-based data extraction is one of the strongest contenders out there.
AI-based data extraction uses OCR technology as its foundation, with extra elements integrated to enhance its functionality, such as natural language processing technology (NLP). NLP technology allows intelligent data extraction technology to actively understand the data’s meaning.
With invoices, for example, understanding the meaning of the text is critical because so many terms share a similar meaning. Take terms such as ‘invoice number’ and ‘order number’ or ‘invoice date’ and ‘invoice pay date’. The subtle phonetic distinctions between each pair conceal significant semantic differences that non-intelligent technology like OCR would struggle to process. Ineffective extraction can then produce potentially disruptive (and costly) consequences downstream.
Newer doesn’t necessarily mean better, so let’s examine where newer technologies like AI may outperform legacy OCR technology.
One distinct advantage OCR invoice capture software may have is its long history in enterprise settings. Because of this, companies looking to integrate this data capture technology into their workflow may experience a better reception from their stakeholders.
Of course, it’s always better to determine what you’re expecting from a potential solution and whether your needs closely match the relative strengths of each technology you’re considering.
Regarding cost, both OCR and AI data capture solutions offer a spectrum of price tags. The majority of solutions will charge a subscription fee (in fact, out of 60 data extraction solutions, only one of them offered per-page pricing).
However, it’s the reliability of the technology that determines its overall value. If you deploy an inflexible and unreliable data capture solution – such as the one our founders experimented with – your employees will waste considerable time and funds.
Another key difference between OCR and AI invoice data capture is that AI works on all documents, sometimes without any training documents (an AI concept known as zero-shot learning). Consequently, if your business’s use case changes or expands outside of invoices, you won’t need to recalibrate the extraction tool.
Ultimately, it’s worth remembering that AI was developed as an alternative to OCR. Although OCR still has merit, you’ll want to take a measured look at the requirements of your data extraction project.
There are two scenarios where OCR-powered extraction engines might outperform AI. Let’s examine two - extraction from structured documents and using unspecialised AI (such as Large Language Models, or LLMs).
Documents with identical structures, also known as structured documents, can be extracted accurately using OCR.
Examples of documents include:
The visual structure of these documents - i.e. the sections and fields - is regimented, meaning OCR can effortlessly lift the content.
Of course, not many financial documents are structured, with unstructured documents like annual reports providing a particular challenge to OCR. Invoices are semi-structured, as they have consistent information (date, number, address, etc.) - but their formats and structures will vary.
You’ll likely have heard of LLMs. Some of the most popular models in 2025 include ChatGPT, Gemini, Claude, Grok and so on.
These LLMs can be used to extract data through prompting. Yet, they are not reliable data extraction tools. For example, we found that for every page of financial data we uploaded, all LLMs would return at least one hallucination - plausible yet fictitious pieces of information. Though OCR can return errors or miss data, at least these types of mistakes are generally easily detectable.
A fundamental technology in AI invoice extraction tools is OCR. However, AI technologies mitigate the issues associated with traditional OCR technologies - such as issues with poor-quality or unstructured invoice data. How exactly can it do so?
Firstly, AI offers Natural Language Processing (NLP) capabilities that understand the language’s meaning, rather than its appearance. Consequently, if OCR is unable to recognise data, NLP technologies are able to infer what it could mean based on its context/
Secondly, computer vision (which aims to replicate human-like understanding of images and videos) helps AI-powered extraction systems understand the visual structure of invoices. Computer vision-enabled technology can detect tables, company logos and other layout elements - segmenting invoice information and mapping it to the correct fields.
Consequently, computer vision forms AI systems that can accurately handle scanned images, rotated documents or multi-column layouts.
Creating a tech stack can help with complex integrations and workflows. An example of an automated workflow might involve:
Therefore, it’s essential that an invoice data extraction solution be able to integrate with other software. Examples of these software include QuickBooks, Xero, SAP, Oracle NetSuite, PayPal and Stripe, etc.
There are various ways of integrating invoice extraction software with other applications - in particular, API and other connective tools.
Developers can establish secure API connections between software systems, enabling seamless data exchange. Technically speaking, this involves an API client (i.e. the invoice extraction tool) initiating a request, which is then processed and responded to by an API endpoint (the other software).
A successful API connection should be:
If your firm doesn’t have a technical team, another option for connecting software is through workflow automation software.
Workflow automation software has become a $20 billion+ market, with the most well-known tool most likely being Zapier. By logging into these apps and setting up triggers and actions for the workflow, you can quickly connect invoice extraction tools with a variety of other products.
For example, our technology was integrated with Integration Platform as a Service (iPaaS) Workato in order to automate invoice extraction for Novuna Business Finance for the first time.
Transcribe is our extraction tool specialised for invoices. It’s based on AI that is trained on a document store of 25 million documents, ensuring accuracy and speed across all invoice types. Here’s how you can use Transcribe to extract data from invoices in seconds, not minutes.
Click, or drag and drop the files from your device.
Does everything seem in order? Our AI leverages proprietary algorithms to read your document like a human (except even more accurately…).
Download the invoice data straight to your device.
It’s also easy to connect to Transcribe via REST API.
Interested in trying Transcribe for yourself? Learn how to get in touch below.
Evolution AI automates data extraction from invoices for large enterprise clients, such as Novuna Business Finance and DF Capital. Our automated data extraction software for invoices delivers cost-effectiveness and scalability, helping our clients reach milestones like:
To speak with a member of our financial data project team about how AI-based invoice data extraction could become a part of your business’s roadmap, book a demo or email us at hello@evolution.ai
