Line items are detailed entries in such financial documents as financial statements, receipts, invoices, budgets, etc. They should lineate/segment expenses or income into easily readable sections.
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The problem with line items is that they’re not always in a usable format. For example, you might want to analyse financial statement data in Excel when they’re ‘locked’ in a PDF. You would then need to extract the line items from the PDF document.
Line item extraction refers to capturing specific, detailed information from a document, where you can then compile the captured data into an actionable format (e.g. Excel or an internal database).
As for the methods of extraction available? You can choose among several common approaches, including the following: manual data extraction, Optical Character Recognition (OCR) and AI-powered tools.
Manual data entry means manually extracting line items into an actionable format. The problem with manual data entry methods is that their success rates depend on human operators’ attention spans and accuracy levels. Generally, research shows that humans can extract data, such as line items, with a 1% error rate. However, that may be a best-case scenario.
Businesses routinely waste hundreds of thousands of work hours on manual data entry. Luckily, there are two alternatives: Optical Character Recognition (OCR) and AI-powered line item extraction.
Unlike humans, automated solutions, such as those based on OCR technology, don't experience fatigue or complacency. The cost of the convenience of automation is that its performance may be comparatively lacklustre. Studies around OCR have revealed varying accuracy rates, often between 87% to 95%.
However, these accuracy rates are relatively low, especially given the precision required for financial or legal documents. For example, if you upload 5 documents with 20 line items, on average, an OCR engine will extract between 5 and 13 of these line items inaccurately.
Even the smallest error can have catastrophic consequences for the bottom line of an enterprise company. For example, in the infamous ‘Ghost Stock’ incident, a clerical error at Samsung Securities led to employees receiving 1,000 company shares instead of 1,000 KRW in dividends, accidentally issuing stock worth over 112 trillion KRW.
Therefore, firms shouldn’t settle for even 95% accuracy when completing sensitive tasks like extracting line items. Potentially, the most frustrating aspect of OCR technology is knowing that improving its accuracy is not a quick fix, as several factors are causing it, such as:
Adding other technology, such as computer vision, machine learning mechanisms and natural language processing (NLP), can compensate for OCR’s performance with visually ambiguous images. For example, if you upload a crumpled and blurred photo of an invoice, OCR would struggle to identify and read the line items accurately. However, (by adding computer vision), the system will first straighten and enhance the image. Machine learning models trained on thousands (or hundreds of thousands) of similar invoices can predict what the distorted characters likely are. NLP can interpret abbreviations or incomplete words (e.g. recognising 'mchry' as 'machinery').
As we’ve written (about) extensively, AI virtual agents are extremely unreliable when handling financial data. Models like ChatGPT and Gemini are liable to generate hallucinations, strict usage limits and other performance blockers. Hallucinations can have serious legal, financial and moral consequences without careful review and correction. Usage limits may also make AI virtual agents unsuitable for enterprise-level use.
However, specialised solutions now exist for extracting line items from financial documents. Such AI solutions use carefully vetted training data (e.g. proprietary document stores), making them far less likely to hallucinate. Therefore, AI generally outperforms OCR and manual data extraction, with some commercial solutions guaranteeing complete accuracy.
Let’s say you want to extract the line data from a receipt. Which approach would work best?
A specialised AI solution would likely perform the best in these instances. That’s because the line items of a receipt are likely to contain semantic nuances (e.g. abbreviations) and visual anomalies (e.g. creases or shadows). AI is trained to recognise patterns and interpret the linguistic semantics, meaning it doesn’t just ‘read’ receipts – it understands them.
Why not test AI and OCR tools for yourself? It’s easy to find dummy receipts and invoices to test various line item extraction tools.
Here, we’ll show you how to use our tool, Transcribe, to extract the line items from invoices.
Log in to Transcribe using a magic link sent straight to your email.
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Click the dropdown box and select ‘Invoice’ (this tells the model that the data will likely conform to a standard invoice format). Then, drag or drop or click the ‘Upload documents’ box to select the desired invoice(s).
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Which format would you like to receive the outputted line items – Excel, CSV or JSON? Head over to the ‘Output’ tab, select the desired format and download the files instantly to your device.
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End to end, the process should take no more than 30 seconds. However, we offer options for faster, automated integration (e.g. via REST API), which means you don’t have to upload documents manually. Contact our financial data project team today to learn how to accurately and affordably extract line items from your financial documents.
Evolution AI’s multiple award-winning data extraction solutions extract line items from documents quickly and accurately. Why choose Evolution AI in particular?
Try Evolution AI today by booking a demo or emailing us at hello@evolution.ai.
