OCR Accuracy Guide: How to Ensure Accuracy and Improve Results

What is meant by “OCR accuracy”?

OCR accuracy refers to the ability of software to transform images of text into editable, accurate digital text. Higher accuracy means fewer errors and less manual intervention, which increases efficiency and confidence in the automation process.

Accuracy is affected by several factors, including:

• Document quality: Poor lighting, blurring, or smudges reduce accuracy.
• Fonts and formatting: Complex or decorative fonts can create difficulties for OCR systems.
• Language and symbols: Multilingual documents or documents with uncommon symbols require specialized OCR capabilities.

Common Myths About OCR Accuracy

Despite the growing adoption of the technology, some misconceptions about OCR accuracy persist. Here are some of the most common myths:

• “OCR works great on any document.” While OCR technology has improved, accuracy is highly dependent on the quality and complexity of the document.
• “All OCR tools handle handwriting without a problem.” Handwriting recognition is a specialized function that requires advanced AI capabilities.
• “OCR accuracy does not change over time.” In reality, accuracy improves with continuous learning and system updates that adapt to data.

Understanding these myths helps businesses set realistic expectations and make more informed decisions when selecting OCR solutions.

How to Calculate OCR Accuracy

To measure OCR accuracy, you need to compare the OCR output to the “true truth” (i.e. a perfect version of the text). Common methods for measuring accuracy include Character Error Rate (CER), Word Error Rate (WER), and Line Error Rate (LER). These measure the percentage of incorrectly recognized characters, words, or lines compared to the original content.

• Character Accuracy Rate (CAR): This is calculated by dividing the incorrect characters by the total number of characters in the reference text. For example, if an OCR tool correctly extracts 950 characters out of 1000, the CAR will be 95%.
• Word Accuracy Rate (WAR): This is useful for documents such as invoices or packing lists, where words contain crucial information. This is calculated as the number of incorrect words compared to the total number of words in the reference text.
• Line Error Rate (LER): This is calculated by dividing the incorrect lines by the total number of lines in the reference text.

Industry-specific considerations for OCR accuracy

Every industry and business department may have unique needs for OCR accuracy, but most deal with “transactional” documents, which are documents that support transactions between businesses or between a business and an individual. Here are some examples:

• Financial Documents: Accurate data extraction from invoices and financial reports is crucial to avoiding costly errors.
• Customs Documents: Properly managing shipping labels, customs forms, and packing slips is essential to a smooth supply chain operation.
• Sales Orders: Slow order processing can cause frustration for both your team and your customers, and errors in order processing can lead to duplicates, lost assets, delays, or incorrect deliveries.
• Healthcare and Insurance Documents: Accurate processing of prescriptions, medical records, and insurance claims is critical, as even small errors can have serious consequences.

How does OCR work with handwriting?

Handwriting recognition has historically been a challenge for OCR systems. While standard OCR struggles with cursive or irregular writing, AI-powered OCR tools have made great strides. These tools:

• Use neural networks to analyze context and predict characters.
• Work better with printed letters than cursive writing.
• They may still require manual validation for highly variable handwriting.

If handwriting is critical to your workflow, look for OCR solutions specifically designed for handwriting recognition.

Is it possible to improve OCR accuracy?

OCR accuracy is not fixed. With the right strategies and tools, you can significantly improve the performance of your OCR system. Here are some best practices to consider:

1. Improve document quality:
   Use high-resolution scans (300 DPI or higher).
   Ensure that documents are well-lit and free of blemishes.
2. Pre-process documents:
   Apply techniques such as noise reduction, binarization, and skew correction before feeding documents into the OCR system.
3. Continuous training and dictionaries:
   Train your OCR system on a representative dataset that includes edge cases, to ensure it evolves with your needs.
   Use dictionaries to help the software make educated predictions.
4. Advanced OCR technology:
   Many legacy OCR systems allow you to create models to handle specific document formats, but these require ongoing maintenance. It is better to opt for tools based on artificial intelligence and machine learning, which are better at adapting to different fonts, formats, and languages.

Build an in-house OCR solution?

Building an in-house OCR solution is possible, but it requires significant resources, technical expertise, and time. Before you embark on this journey, consider a few key questions:

• Does building an OCR solution align with our strategic priorities?
• Do we have the expertise and resources to develop and maintain the system?
• Can we achieve our goals on time and on budget?

What to Consider When Buying an OCR Tool

If you have decided not to build an in-house solution, but are looking for an OCR tool, here is a checklist to help you choose the right solution:

1. Assess your needs:
   What are your current document processes? Which ones can be automated?
   Which documents do you want to process?
   Are there synergies with other departments?
   What business outcomes do you want to achieve?
2. Accuracy metrics:
   Does the tool provide clear metrics like CAR and WAR?
   Can you test it with your documents?
3. Integration and scalability:
   Does the OCR tool integrate with your existing systems?
   Can it scale to handle a growing volume of documents?
4. Personalization and training:
   Can you train the software to recognize custom fields?
   Does the AI ​​learn from document to document?
5. Security and compliance:
   Does the tool comply with data protection regulations, such as GDPR?
   Does it offer encryption and data protection?
6. Cost and ROI:
   Is the price in line with your budget?
   Does the tool offer a demonstrable ROI by reducing manual work and errors?