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Invoice Processing Automation

Automating Invoice Processing – the Ultimate Guide


What is automated invoice processing?

Automated invoice processing is a technology which changed the way companies manage their accounting documents. The technology simplifies the tedious and error-prone accounts payable process – it automatically extracts data from invoices making the process more efficient. This has now changed from the traditional manual data entry to today’s complex software solutions which are part of every efficient business process these days. 

How does it work?

In this guide we will look at the different aspects of automated document processing. We will look into every step of the process – from the initial reception of a document to the final data export and import into a financial system. We will discuss the challenges related to automating the automated invoice processing and how the current technologies like machine learning and pattern recognition are used to overcome these challenges. 

Document reception and digitalization 

Receiving physical documents: Automated invoice processing often starts with physical paper documents. These are converted to digital format using scanning technology. Scanners with automatic feeder devices can efficiently process a large number of paper documents. The scanning settings are important here: usually people use 300 dpi (dots per inch) to balance between the file size and the OCR accuracy. Scanning in color is not always needed but it does not hurt. However, most of the time you don’t have any control over the scanning process, as the companies who send out invoices have already scanned them at lower resolutions and high levels of compression in order to save on storage space. Such factors can influence the reading quality but the invoice automation software systems are designed to handle such low quality documents. Additionally, any low quality recognitions are optionally marked for review and correction by an operator.

Handling Emails and Electronic Invoices: Invoice processing automation systems are also integrated with your email provider to automate the processing of invoices received by email.This usually involves the configuration of a special email address to receive the incoming invoices. The software monitors this mailbox and extracts the attached documents. Various file formats are supported such as JPEG, TIFF, PNG and GIF. The system can also optimally handle invoices that are part of the email’s body. The software should support modern authentication schemas such as OAuth 2 to integrate with popular email providers such as Microsoft Outlook (including Exchange) and Gmail.

Web portals and online submission: Many companies receive their invoices through web portals where the vendors directly upload them. An Invoice processing system would automatically extract the content from such documents into machine readable format eliminating the need for OCR – this increases both the speed and accuracy of the process. 

APIs and Integrated Systems: An API is a set of commands which allow different software systems to communicate with each other. For companies who receive their invoice through various digital channels or directly into existing ERP systems, APIs facilitate the integration and allow for the documents to be directly imported into the Invoice automation system from other software applications.

File Format Considerations: An invoice processing system handles various file formats, including PDF, JPEG, TIFF, PNG, GIF, Word documents – DOC and DOCX, Excel spreadsheets – XLS and XLSX and HTML files, accommodating the diverse methods of invoice creation and transmission.

Pre-processing for Optimal OCR: Before the documents are OCR processed they often undergo pre-processing which includes de-skewing (correcting tilt), noise removal (such as stray marks or shadows) and contrast adjustment to make sure the text is readable on the background. This is crucial for OCR accuracy. 


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  • Document Classification
  • Data Extraction
  • Continuous Learning
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Optical Character Recognition (OCR)

The basic principles of OCR: OCR technology is about converting various types of images containing text (such as scanned documents or photos) into machine readable and editable text data. At its core OCR includes several stages starting with the image pre-processing to increase the text clarity and finishing with the actual character recognition and conversion. 

Image Pre-processing: Before OCR can effectively interpret the text the image undergoes pre-processing. This includes adjustment of the brightness and contrast, noise reduction, deskewing to correct any tilt in the image. The goal is to get a clear image where the text is as readable as possible for the OCR engine. 

Text Detection and Segmentation: The next step is to detect where the text is in the image. This includes segmenting the image into regions that contain text and those that don’t. Advanced Invoice Processing Automation systems use techniques such as connected components analysis to distinguish the characters and words from the background. 

Character Recognition: After the text regions have been identified the OCR engine applies pattern recognition to recognize individual characters. This is usually done using machine learning algorithms which are trained on vast datasets for various fonts and handwriting styles. These algorithms analyze the shapes and lines of each character to match it with the correct letter or number. 

Contextual Analysis and Error Correction: After the initial character recognition the OCR software does contextual analysis to correct any errors. For example, if a certain character has been recognized incorrectly the software can use the context of the surrounding words and characters and a dictionary to make a more accurate guess. This step is crucial for improving the overall accuracy of the OCR process.

Challenges in OCR: Developing an OCR engine like Tesseract includes overcoming certain challenges. The text in an image can vary greatly in terms of font, size, color and background contrast. Handwritten text adds an additional level of complexity because of the variations in the individual handwriting styles. OCR systems must be robust enough to handle these variations and maintain a high level of accuracy. 


Classification in Document Processing: Document classification is the initial step where incoming documents are identified and categorized. This stage is very important for determining the right processing flow for each document. 

The Classification Process:As documents and to the system the classification and engine processes each of them to determine its class. This includes distinguishing between different kinds of financial documents such as invoices, purchase orders receipts or other related documents. The system assesses features like format, layout, and specific keywords to accurately categorize the document.

Techniques Used in Classification: This process often uses machine learning models trained on large datasets of various document types. The models analyze various features of the document such as layout elements and text patterns to classify it. For example, while a document including the words “Invoice number” or “Total amount due” is typically an invoice, the presence of “Order number” could lead to confusion as it might be present in both invoices and POs. This complexity makes it necessary to use advanced neural network algorithms for more nuanced and accurate classification. 

Significance of Accurate Classification: Accurate classification is important for efficient document processing. It ensures that every document type is correctly routed to the subsequent steps, such as data extraction. Correct classification is especially important in environments where various types of documents are processed, and each type requires a specific procedure. 

Data Extraction

Data extraction, sometimes called data capture, is the process of extracting specific information from a document. This step focuses on reading specific details such as vendor names, invoice numbers, dates, line item descriptions, quantities, unit prices and total amounts. 

Advanced Extraction Techniques: This stage uses advanced data extraction techniques that go beyond simple pattern recognition. This involves understanding of the context, where the system recognizes that a certain piece of text is for example an amount due to its placement and format, even if it does not match a predefined location or pattern. 

Machine learning: Advanced data extraction systems use machine learning models that have been trained on diverse sets of invoice data. These models are good at predicting where key information is located in an invoice, even when the layout varies. This adaptability is especially important when processing non-standard or complex invoices. 

Challenges of Table Detection: Detecting and accurately extracting data from tables in invoices presents its own set of challenges. Invoices often contain tables with different layouts, column arrangements, and types of information, such as lists of products or services, with corresponding prices and quantities. Some tables might have merged cells, varying row heights or span across multiple pages, all these complicating the capture process. Also, subtle differences in shading, lines or text alignments in tables can impact the accuracy of data extraction. Advanced invoice processing systems must be equipped with sophisticated algorithms to understand these varied table formats and accurately extract the tabular data..

Accuracy and Error Handling: Accuracy is of a major importance in data capture, to ensure financial data is correctly processed. The system typically includes configurable validation checks such as verifying the total amount value against the sum of the line item totals, or the PO number against a database table. When discrepancies are found, the system can flag them for human verification. 

Training and Learning from User Feedback: A key aspect of data capture in invoice processing automation is the ability of the system to learn and improve over time. This is achieved by continuous learning based on user feedback. When users correct or validate captured data their input is fed back to the machine learning models. This process allows the system to adapt to specific invoice layouts it has not seen below and correct any errors, improving its performance. With time, the overall capture accuracy improves. 

Exporting Extracted Data

Diverse Options for Data Export: After the extraction of the invoice data, the next step is typically exporting this data into other systems or applications for further processing and storage. This phase involves converting the information into various formats and integration with various business software systems.

Direct Integration with Accounting Systems: One method is direct integration with accounting software or Enterprise Resource Planning (ERP) systems. This approach automatically sends captured data into accounting systems such as QuickBooks, SAP, or Oracle. This integration ensures smooth transition of invoice data into the accounting workflow. It facilitates common tasks such as three way matching.

Exporting to Databases: Another option is transferring the captured data into a database. Depending on the company’s data infrastructure this can be a relational database like SQL Server, MySQL, or a NoSQL database such as MongoDB. Exporting to a database is particularly useful for businesses that need to store a large volume of invoice data for long-term analysis, reporting, or for integrating with other data-driven business processes. 

Export to Standard File Formats for Automated Integration: Exporting data to standard file formats like CSV, XML, or Excel is a common choice for companies, especially when downstream software systems are able to import such files automatically. For example, companies might have configured automated workflows where CSV or Excel files are automatically imported into accounting, analytics or document management systems.

When dealing with XML, a key technique is XSLT (Extensible Stylesheet Language Transformations). XSLT is used to convert XML documents into other XML documents, or different formats, based on configurable rules. This transformation is especially useful in customizing the XML output to fit the exact requirements of various downstream systems. By using XSLT, companies can ensure that the XML data exported from the invoice processing system is formatted correctly for seamless integration with the receiving applications.

A Comprehensive Guide to Invoice Processing Automation

In this guide, we’ve shed light on the processes and technologies behind invoice processing automation, going beyond a surface-level understanding. By delving into the intricate details of optical character recognition (OCR), document classification, data capture, and data export, we’ve explained the major components that make this transformative solution possible. If you need further information or wish to explore the implementation of invoice processing automation in your business, please don’t hesitate to contact us. SmartSoft’s team is here to support your journey towards greater cost efficiency, accuracy, and success in the digital age.