Many businesses still rely on manual processes to enter invoice data into accounting or ERP systems. Even when OCR software is used to extract text from documents, teams often find themselves reviewing and correcting the extracted information before it can be posted into their systems.
Automating invoice processing aims to remove this manual step. Instead of reading invoices and typing data into a system, automation tools extract the information from incoming documents and convert it into the structure required by the company’s internal software.
The challenge is that invoices vary significantly between suppliers. One company may send a simple invoice with clear labels such as "Invoice Number", "Date" and "Total". Another may use a completely different layout with different naming conventions and formatting. Because of this variation, simply extracting text from a document is rarely enough to automate the entire workflow.
This is why many OCR solutions still require manual review before data can be used. As discussed in our article Why OCR Invoice Processing Still Requires Manual Review, extraction alone does not guarantee that the data is correct or structured in the way your system expects.
Automation platforms solve this by combining OCR with additional logic. Instead of only reading text, the system applies rules and transformations that convert the extracted information into a consistent format. For example, an invoice field labelled "Invoice No" might be mapped to a database column called INVNUMBER, while "Invoice Date" might be mapped to INVDATE.
Handling invoice line items is another important part of automation. When invoices contain tables of products, quantities and prices, the structure can vary widely between suppliers. This variation often causes OCR tools to misinterpret rows or columns. We explore this problem further in Why OCR Struggles With Invoice Line Items.
Document automation platforms take a different approach by allowing the system to be trained using real documents. In Harold, this process is handled through DocuTrain. Users upload example invoices and define the exact data structure they want the system to produce. Once trained, the platform remembers how to interpret similar documents in the future.
This training approach allows businesses to automate invoice processing even when documents vary significantly between suppliers. Instead of creating rigid templates for each layout, the system learns the patterns within your documents and applies the same structure each time new invoices arrive.
Rules can also be applied to validate extracted data. For example, the system can check that the invoice total equals the sum of the line items, confirm that a supplier exists in the system or automatically assign accounting codes based on predefined logic.
By combining OCR extraction, document training and validation rules, invoice processing can move from a manual task to a largely automated workflow. Teams no longer need to review every invoice individually. Instead, they only need to look at the small number of documents that fail validation checks or contain unusual data.
The goal of document automation is simple. Train the system once using the documents your business already receives, allow it to extract and structure the data automatically, and only intervene when something unexpected appears.
When implemented correctly, this approach allows businesses to significantly reduce manual data entry while improving the accuracy and consistency of their financial data.