OCR technology has made it significantly easier for businesses to extract information from documents such as invoices, purchase orders and receipts. By automatically reading text from a document, OCR systems can reduce the need for manual data entry and speed up document processing.
However, while OCR can extract text quickly, it is not always perfect. Many businesses using OCR invoice processing tools encounter common extraction errors that still require manual review before the data can be trusted. These errors often occur because documents vary widely in layout, formatting and structure.
One of the most frequent issues is incorrect field recognition. OCR systems may incorrectly identify where a value begins or ends, particularly when documents contain dense text or complex formatting. For example, an invoice number might be interpreted as part of a purchase order reference, or a date may be extracted incorrectly when multiple dates appear on the document.
Another common error occurs when OCR systems misinterpret supplier names or addresses. Invoices often contain multiple pieces of company information, including billing addresses, shipping addresses and supplier contact details. Without clear context, an OCR engine may capture the wrong company name or location.
Tables and line items are another area where extraction errors frequently appear. Invoice line items often contain multiple columns of information such as product descriptions, quantities and prices. When invoice layouts vary between suppliers, OCR systems can struggle to determine which values belong in which column. This can lead to quantities appearing in the wrong field or totals being incorrectly calculated. We explored this issue in more detail in our article Why OCR Struggles With Invoice Line Items.
Totals and tax calculations can also cause problems. Some invoices display subtotal, tax and total amounts in different positions or formats. OCR systems may extract these values correctly but fail to recognise which number represents the final invoice total. As a result, finance teams often need to manually verify that the extracted totals match the actual document.
These types of extraction problems are one of the reasons why OCR workflows often include a manual validation stage. As discussed in Why OCR Invoice Processing Still Requires Manual Review, simply reading text from a document does not guarantee that the extracted data is correct or structured in a way that matches internal systems.
Document automation platforms aim to reduce these issues by combining OCR with additional logic and validation. Instead of simply extracting text, automation systems can apply rules that check whether the extracted information makes sense. For example, a rule might confirm that the invoice total equals the sum of the line items, or that the supplier name matches a known vendor in the system.
At Harold, this approach is implemented through a training process that allows businesses to define how their documents should be interpreted. Using the DocuTrain feature, users can train the system on real examples of the documents they receive. This allows Harold to learn how specific invoice formats should be interpreted and converted into the structure required by the company’s accounting or ERP system.
Over time this reduces the number of extraction errors and minimises the need for manual corrections. Instead of reviewing every document individually, teams can focus on the small number of cases where something unusual appears in the data.
OCR has made document extraction faster, but it is only one part of the automation process. By combining extraction with validation rules and document training, businesses can move beyond simple OCR and towards a more reliable document automation workflow.