Understanding OCR Accuracy
OCR technology has long been essential for data digitization, but AI advancements have transformed its potential. For finance and IT leaders, this brings both opportunity and challenge: How do you ensure your tools meet modern accuracy standards? In this guide, I’ll explore what OCR accuracy really means, how to measure it, ways to improve it, and what to look for in an OCR solution.
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What is OCR accuracy?
OCR accuracy refers to how well software can convert images of text into editable and accurate digital text. High accuracy means fewer errors and less manual intervention, leading to increased efficiency and trust in the automation process.
Accuracy is influenced by several factors, including:
- Document quality: Poor lighting, blurs, or smudges reduce accuracy.
- Fonts and formatting: Complex or decorative fonts can confuse OCR systems, as well as ordinary fonts with very similar looking characters.
- Language and symbols: Multilingual documents or those with uncommon symbols require specialized OCR capabilities.
Common myths about OCR accuracy
Despite its growing adoption, misconceptions about OCR accuracy persist. Let’s debunk a few:
“OCR works perfectly with any document.” While OCR technology has advanced, accuracy depends heavily on document quality and complexity.
“All OCR tools can handle handwriting seamlessly.” Handwriting recognition remains a specialized feature requiring advanced AI capabilities.
“OCR accuracy doesn’t change over time.” Accuracy improves with continuous learning and system updates tailored to your data.
By understanding these myths, businesses can set realistic expectations and make informed decisions about OCR solutions.
How to calculate OCR accuracy
To measure OCR accuracy, you’ll need a comparison between the OCR output and the “ground truth” (aka a perfect version of the text). This is typically measured by metrics such as character error rate (CER), word error rate (WER), or line error rate (LER). Basically, these quantify the percentage of incorrectly recognized characters, words, or lines compared to the original content.
1. Character Accuracy
Formula: Correct characters divided by total characters in the reference text.
E.g., If an OCR tool extracts 950 characters correctly out of 1,000, the character accuracy is 95%.
2. Word Accuracy
Word Accuracy Rate is particularly useful when working with documents like invoices or packing lists, where words often hold key information.
Formula: Correct words divided by total words in the reference text.
E.g., If an OCR tool extracts 92 words correctly out of 100, the word accuracy is 92%.
3. Line Error Rate (LER)
Formula: Incorrect lines divided by total lines in the reference text.
Document-specific considerations for OCR accuracy
Different industries and even departments within an organization face unique challenges and requirements when it comes to OCR accuracy, but most of them deal with what we call “transactional documents” (documents that support transactions between businesses, or between a business and an individual). Here are some examples:
- Financial documents: Precise data extraction for invoices and financial reports is critical to avoid costly errors. Here’s a real-life example from Master Trust Bank of Japan.
- Customs clearance documents: The ability to handle shipping labels, customs forms, and packing slips efficiently ensures smooth supply chain operations. Look at how Port of Rotterdam automated the processing of tonnage certificates.
- Sales order forms: Slow order processing is frustrating for both your team and your customers – especially when peak season hits. And errors in the processing of these sales orders often mean duplicate orders, misplaced resources, late or incorrect deliveries, etc.
- Healthcare & insurance documents: OCR must accurately process prescriptions, patient records, and insurance claims, where even minor mistakes can have significant repercussions.
Understanding your specific document needs will help you evaluate OCR tools more effectively.
Is it possible to improve OCR accuracy?
OCR accuracy is not set in stone. With the right strategies and tools, you can significantly enhance the performance of your OCR system. Here are some best practices you should consider to achieve the accuracy you’re looking for.
1. Enhance document quality
- Use high-resolution scans (300 DPI or higher).
- Ensure documents are well-lit and free from smudges or stains.
2. Preprocess documents
- Apply techniques like noise reduction, binarization, and skew correction before feeding documents into the OCR system.
3. Continuous training & thesauruses
- Train your OCR system on a representative dataset that includes edge cases, ensuring it evolves with your needs.
- Consider using dictionaries and thesauruses to help the software make educated guesses.
4. Leverage advanced OCR technology
Many legacy OCR systems allow for template creation to handle specific document layouts, improving accuracy for recurring formats. However, templates require heavy maintenance and involvement from your IT teams. Opt for tools powered by AI and machine learning, which adapt better to varying fonts, formats, and languages.
How does OCR perform with handwriting?
Handwriting recognition has historically been a challenge for OCR systems. While standard OCR struggles with cursive or inconsistent handwriting, AI-powered OCR tools have made significant advancements.
These tools:
- Use neural networks to analyze context and predict characters.
- Perform better with block letters than cursive scripts.
- May still require manual validation for highly variable handwriting.
If handwriting is a critical part of your workflow, look for OCR solutions specifically designed with handwriting recognition capabilities.
Should you build your own OCR solution?
Building your own OCR solution is possible, but it requires significant resources, expertise, and time.
Questions to consider before building
- Is building an OCR solution aligned with our strategic priorities?
- Do we have the technical expertise and resources to develop and maintain the system?
- Can we achieve our goals within budget and on time?
- Will our custom solution be scalable and adaptable to future needs?
Challenges of building your own OCR software
If you’re considering building, here’s what you’ll need to address with your IT team:
- Infrastructure: Develop systems for ingesting documents from multiple sources, such as emails, APIs, and scanners.
- Accuracy optimization: Implement advanced algorithms and training mechanisms to ensure high accuracy.
- Custom interfaces: Design user interfaces for data verification and correction.
- Maintenance: Establish pipelines for training, testing, and refining the system over time
- Security: Ensure compliance with regulations and implement robust data protection measures.
Your checklist when shopping for OCR tools
So you’ve decided against building your own internal solution, and are in the market for an OCR solution. First, you may want to ask yourself whether OCR is really what you are looking for. Then, when evaluating OCR tools, use this checklist to ensure you select the right solution:
First, assess your needs
- What do your current document processes look like? Which ones can be automated?
- Which documents do you want to process?
- Are there synergies with other departments?
- Which business outcomes are you looking for?
Accuracy metrics
- Does the tool provide clear metrics for character and word accuracy?
- Can you test the tool with your own documents?
Note: Don’t compare apples with oranges. When comparing OCR tools, ensure you’re evaluating accuracy under similar conditions (e.g., document type, language, and quality).
Integration and scalability
- Can the OCR tool integrate with your existing systems (e.g., ERP)?
- Is it scalable to handle increasing document volumes?
- Does the OCR handle multilingual documents?
- Can it process a variety of formats, including PDFs and images?
Customization and training
- Can you train the software to recognize custom fields?
- Does the AI learn from one document to the other?
- Does the vendor provide support for training the system on your specific data?
Security and compliance
- Is the tool compliant with data protection regulations like GDPR?
- Does it offer robust encryption and secure processing?
Cost and ROI
- Is the pricing aligned with your budget?
- Does the tool offer demonstrable ROI by reducing manual work and errors?