A Complete Guide to Invoice OCR Processing: Benefits, Methods (+Best Tools in 2024)

Manual invoice processing is a business problem that consumes valuable time and resources. The traditional approach of employees manually entering data from paper invoices into systems is time-consuming and prone to human error.

A study by Levvel Research found that approximately 62% of invoices are still processed manually. Inefficiencies cost businesses thousands of dollars annually. As Billentis estimates, errors in invoice processing can cost up to $30 per invoice. 

These challenges underscore the need for a more efficient solution. Optical Character Recognition is a technology that automates data extraction from invoices and significantly reduces errors to speed up the entire process.

What is Invoice OCR?

Optical Character Recognition invoicing helps with this. It involves training a template-based OCR model for specific invoice layouts, setting up input paths for these invoices, extracting data, and integrating the extracted data with a structured database. 

Invoice OCR works by scanning the invoice to create a digital image. OCR software then processes this image to identify and extract the text and numbers. Advanced Invoice OCR solutions use AI and machine learning to recognize various formats and layouts of invoices. 

This semi-automated data extraction technique can be used to extract field-specific information from fixed template documents. OCR is not able to extract “context-specific” information from documents; Intelligent Document Processing overcomes this limitation.

These technologies help the system learn and improve over time, making it increasingly accurate in identifying and extracting the necessary data. Automation speeds up the process and reduces errors to more efficient invoice management.

How Does OCR Invoice Processing Work?

OCR invoice processing automates extracting key data from invoices by following a structured workflow. Here’s a step-by-step breakdown of how it works:

1. Scanning and Uploading

• The first step in OCR invoice processing is scanning and uploading the invoice document. Invoices can be in formats like PDFs or photos taken with a smartphone. Document preparation is crucial at this stage, as the quality of the input can significantly impact the accuracy of the OCR process.
• High-resolution scans are preferred, and documents should be clear and free of obstructions that might interfere with text recognition.

2. Image Pre-processing‍

• Non-scanned PDF invoices are scanned and turned into JPG files with a resolution of 600x600x3 and 300 DPI. Multiple pre-processing techniques are applied. Then, the images are parsed to a deep-learning model for training.
• Techniques like noise reduction are applied to remove unwanted artifacts or background interference. Image pre-processing is used to correct any misalignment in the document to ensure the text is aligned correctly for recognition. 
• To make the text stand out more clearly, contrast adjustment and binarization (converting the image to black and white) are performed. 

3. Text Detection

• ‍Once we have the complete data, it is fed to the detection model to recognize the tables and forms within the input images. The OCR software identifies the regions within the document that contain text. 
• This step is crucial because the text is often spread across multiple sections with different fonts and orientations. The software uses algorithms to detect these text regions and segment them accordingly. 

4. Text Recognition

• ‍This step involves identifying the location of one or more objects and delineating their extent by bounding boxes. The OCR software identifies the regions within the document that contain text. 
• This step is crucial because the text is often spread across multiple sections with different fonts and orientations. The software uses algorithms to detect these text regions and segment them accordingly. This step lays the foundation for the next phase, where text recognition occurs.

5. Texts/Information Extraction

• ‍Extract the text from the detected regions. We currently use Tesseract-OCR (an open-source program to extract data from the images).
• The OCR software converts the detected text into a machine-readable format. This involves recognizing each character in the text regions identified earlier. 
• Modern Invoice OCR systems use AI and machine learning algorithms to handle different fonts, languages, and even handwriting with high accuracy.

6. Data Validations and Verifications

• The data undergoes a series of validations and verifications to ensure accuracy. Cross-referencing the extracted data with existing records or databases and flagging any discrepancies for review. 
• Advanced Invoice OCR systems can also apply business rules to validate data. ensuring that the invoice date falls within a certain range or that the tax calculations are correct.

7. Data Export

• After validation of the data the extracted data can be exported in various formats. This data can then be integrated into accounting systems and other business applications. 
• The ability to export data in multiple formats ensures that the OCR system can integrate with existing workflows and systems.

8. Archiving

• The final step in OCR invoice processing is archiving the original documents and the extracted data. Archiving is important for record-keeping and compliance with regulatory requirements. 
• Proper archiving ensures that invoices can be easily retrieved if needed. Many OCR systems include automatic indexing and categorization features, making managing large volumes of documents easier.

Check out how Valtatech drives 3x faster data extraction from 20k+ invoices monthly

Methods of OCR Invoice Processing

OCR invoice processing has evolved significantly, offering various methods to suit different business needs. Here's an in-depth look at these methods:

1. Pre-defined Templates

• Pre-defined templates are one of the most traditional methods for OCR invoice processing. 
• This approach relies on templates tailored to recognize specific fields and layouts in an invoice, such as where the invoice number, date, and total amount are typically located.
• Businesses can achieve faster processing times using pre-defined templates since the OCR software knows exactly where to look for relevant data.

2. OCR for Invoice Processing with Machine Learning

• Modern OCR solutions use machine learning to overcome the limitations of pre-defined templates. AI-powered OCR systems can learn from many invoices with different formats and layouts, allowing them to adapt to new invoices more accurately.
• OCR can identify patterns, understand data context, and improve accuracy with each processed invoice. 

3. Cloud-Based

• Cloud-based OCR solutions offer several benefits, such as scalability. Businesses can easily scale their OCR capabilities up or down based on demand without needing to make a significant upfront hardware investment. 
• Cloud-based OCR systems can be accessed anywhere with an internet connection to process invoices and access data remotely. 
• This is especially useful for businesses with distributed teams or those that require operational flexibility.

4. On-Premise

• On-premise OCR systems offer businesses complete control over their data and processing environment. This method is advantageous for organizations with strict data security and privacy requirements.
• The initial cost of purchasing and setting up the necessary hardware and software can be significant. ongoing maintenance, updates, and scaling must be managed internally, adding to the operational burden.

5. Hybrid Solutions

• The OCR processing might be handled in the cloud, while sensitive data is stored and managed on-premise. This approach allows businesses to benefit from the scalability and accessibility of the cloud while maintaining control over critical data.
• Hybrid solutions are particularly useful for businesses that need to process large volumes of invoices but have concerns about data security or regulatory compliance.

6. API-Based OCR

• API-based OCR solutions allow businesses to integrate OCR capabilities directly into their existing software and workflows. 
• Companies can add OCR functions to their accounting systems or custom applications without building the OCR technology from scratch by using APIs.

7. Template-Free OCR

• Template-free OCR systems are highly adaptable and can quickly learn to recognize new invoice formats, making them ideal for environments with a lot of variability. 
• They are often used with machine learning to improve accuracy and reduce errors continuously.
• This diverse range of OCR invoice processing methods allows businesses to choose the approach that best fits their specific needs.

Benefits of OCR for Invoice Processing

Implementing OCR technology for invoice processing offers numerous advantages that streamline operations and improve business outcomes. 

Here’s a summary of the key benefits:

1. Improved Efficiency

One of the most significant benefits of OCR for invoice processing is the dramatic improvement in efficiency. OCR eliminates the need for manual data entry, which is both time-consuming and prone to errors. 

This automation allows businesses to process large volumes of invoices quickly, to free up valuable time for employees to focus on more strategic tasks.

2. Increased Accuracy

OCR technology significantly increases the accuracy of data extraction from invoices. OCR systems can accurately read and extract information, even from complex or unclear invoices.

With advanced AI and machine learning algorithms, modern OCR solutions continually improve their accuracy over time, ensuring that the extracted data is reliable and precise.

3. Cost Reduction

OCR technology helps reduce operational costs. The reduction in manual labor leads to lower staffing costs, and the decrease in errors reduces the costs associated with correcting mistakes. 

OCR can help identify discrepancies and prevent overpayments contributing to cost savings. Businesses can also save on storage costs by digitizing invoices and reducing the need for physical storage space.

4. Faster Payment Processing

OCR speeds up the invoice processing cycle, leading to faster payment processing. When invoices are processed quickly and accurately, businesses can avoid late payment penalties and take advantage of early payment discounts. 

Faster processing also improves cash flow management, allowing businesses to plan better and allocate resources.

5. Better Data Management

OCR facilitates better data management by converting paper invoices into digital format, making storing, retrieving, and analyzing invoice data easier. This digitization allows for integrating invoice data into accounting systems and other business applications, enabling more effective financial reporting and decision-making. 

Additionally, OCR systems often include features for categorizing and indexing invoices, which simplifies document management and retrieval.

6. Enhanced Compliance

Compliance with financial regulations and internal policies is critical for businesses, and OCR technology enhances this aspect of invoice processing. OCR systems can automatically validate and verify invoice data against predefined business rules and regulatory requirements, reducing non-compliance risk. 

By maintaining accurate and easily accessible records, businesses can also ensure they meet audit and legal requirements more effectively.

These benefits highlight how OCR for invoice processing streamlines operations, enhances accuracy, reduces costs, and improves overall business performance.

What are the challenges of OCR Invoice Processing?

OCR, not fully automated, poses several limitations to extracting invoice data and complicates automation implementation. 

Data extracted for the first time from a new invoice format from an OCR software cannot be 100% correct. The reason is different invoice templates and styles, which require the system to be initially familiar with the invoice template.

1. Handling Various Invoice Formats and Layouts

OCR systems often struggle with diverse invoice formats and layouts. An estimated 25% of invoices require manual correction due to variations in design, fonts, and field placement. Template-based OCR systems can miss up to 30% of data points when encountering non-standard layouts.

2. Dealing with Low-Quality Images

Low-quality images, such as blurry or poorly scanned invoices, can hinder OCR accuracy. Even with advanced image preprocessing techniques like noise reduction and deskewing, OCR systems may still need help to extract accurate data from suboptimal images.

3. Ensuring Data Accuracy for Complex Invoices

Invoices with complex structures, including multiple line items and varying terms, pose a challenge for OCR systems. Misinterpretation or misalignment of data can result in errors, requiring manual intervention to ensure accurate processing.

4. Protecting Sensitive Invoice Data

Data security is a top concern, especially when 60% of businesses report handling sensitive information like bank details and confidential pricing in their invoices. Without proper encryption and access controls, there’s a risk of data breaches affecting up to 10% of companies using OCR solutions.

4 Best Invoice OCR API Tools

Here’s a brief overview of some popular invoice OCR API tools for invoice processing:

1. Docsumo

Capabilities

Docsumo offers advanced OCR capabilities designed specifically for document processing, including invoices. It excels at extracting structured data such as invoice numbers, dates, and line items.

Integration Options

Docsumo provides easy integration with various platforms via API, making it suitable for automating invoice workflows within existing systems.

2. Amazon Textract

Capabilities

Amazon Textract is a powerful invoice OCR API  that goes beyond simple text extraction by identifying and understanding the context of data within documents. It handles complex documents like invoices effectively.

Integration Options

It easily integrates with AWS services and other platforms via RESTful API, enabling use integration into existing AWS workflows.

3. Microsoft Azure Form Recognizer

Capabilities

Azure Form Recognizer is a robust OCR tool that extracts data from forms, including invoices, with high accuracy. It uses AI to understand and structure the extracted data.

Integration Options

It integrates smoothly with Microsoft’s Azure ecosystem and other applications through API, making it suitable for businesses already using Azure services.

4. Google Cloud Vision API

Capabilities

Google Cloud Vision API is a versatile OCR tool capable of extracting text from invoices and other documents. It also includes image analysis features like object detection and classification.

Integration Options

It easily integrates with Google Cloud services and other platforms via API, making it a good choice for businesses using the Google Cloud ecosystem.

How Accurate Are OCR Invoicing Solutions? 

OCR accuracy is influenced by image quality, invoice complexity, and the technology used. High-quality images and simpler invoice layouts generally lead to better results, while complex formats can result in higher error rates.

Modern OCR solutions can achieve accuracy rates of up to 98% in ideal conditions. However, for complex invoices, accuracy may drop to around 80%, emphasizing the need for robust technology and ongoing training.

Using Docsumo's sophisticated OCR engine and APIs, you can automate invoice data capture and processing. Minimal configuration, smart extraction, and validation, along with simple integration. Save hours formerly spent manually entering invoice data by using Docsumo's invoice capture software.

Sign up to Docsumo for a trial now!