In today’s digital age, manually transcribing text from scanned documents is impractical for businesses.
This is where OCR (Optical Character Recognition) solutions come in, automating the conversion of images into editable text, making data extraction seamless.
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What is OCR Accuracy?
OCR accuracy is a crucial metric that determines how well software can transform printed or handwritten text from scanned images into digital format.
For instance, when scanning a printed book into a digital text document, OCR accuracy assesses how faithfully the software captures each character and word from the scanned pages, ensuring the resulting digital text closely mirrors the original content’s layout and content.
How to Calculate OCR Accuracy?
Calculating OCR accuracy involves comparing the output of an OCR system to the original source text and determining how accurately it transcribed the content.
Here’s how to calculate OCR accuracy:
Step #1: Obtain Ground Truth Data
You need the original source text (ground truth) that was used to create the document you’re OCR-ing. This serves as the reference for accuracy assessment.
Example: If you’re OCR-ing a printed book, obtain a digital version of the book as the ground truth, which contains the accurate text of the entire book.
Step #2: Perform OCR
Run the OCR software or engine on the source images or documents to convert them into digital text.
Example: Use OCR software to scan each page of the printed book and convert it into digital text. Save the OCR-generated text as a separate file.
Step #3: Text Comparison
Compare the OCR-generated text with the ground truth text character by character or word by word.
Example: Compare a specific page from the OCR output to the corresponding page in the ground truth. Identify differences, such as incorrect characters or words.
Step #4: Count Errors
Identify and count the errors, which can include substitutions (incorrect characters or words), insertions (extra characters or words), and deletions (missing characters or words).
Example: On one page, you find that the OCR output contains three substitutions (wrong words), two insertions (extra words), and one deletion (a missing word).
Step #5: Calculate Accuracy
Use the following formula to calculate OCR accuracy:
Accuracy (%) = [(Total Characters or Words – Total Errors) / Total Characters or Words] x 100
For example, if you have 1,000 characters in the ground truth and the OCR output contains 50 errors, the accuracy would be [(1000 – 50) / 1000] x 100 = 95%.
In the case of that page, if it has 1,000 characters in the ground truth, and you found a total of 6 errors, you can calculate accuracy as [(1000 – 6) / 1000] x 100 = 99.4%.
This accuracy percentage represents how closely the OCR output matches the original text. Higher accuracy indicates better OCR performance.
Additional Things to Consider When Calculating OCR Accuracy
- Some OCR software may provide accuracy metrics directly, simplifying the calculation process.
- Additionally, you can use OCR evaluation tools and libraries that automate this process for large datasets.
How Does OCR Work?
OCR systems use one of the two algorithms to identify the characters in the scanned document:
- Pattern Recognition– The program compares the characters in the document or image with the examples of fed text.
- Feature Recognition– It uses the features of alphabets, numeric digits, or symbols such as angled lines, crossed lines, and curves to recognize characters.
How to Improve OCR Accuracy
Improving OCR (Optical Character Recognition) accuracy is essential for getting more precise and reliable results when digitizing text from scanned images or documents.
Here’s a step-by-step guide on how to improve OCR accuracy:
Step #1: Use High-Quality Source Images
- Ensure source documents are scanned or captured in high resolution.
- Use clean, well-lit, and well-aligned images to reduce noise.
Step #2: Optimize Image Resolution
- Higher resolution images retain more detail and improve character recognition.
- Aim for a minimum of 300 DPI (dots per inch) for printed text.
Step #3: Noise Removal:
- Remove background noise, speckles, or artifacts that can interfere with OCR.
- Image preprocessing techniques like thresholding or filtering can help.
Step #4: Enhance Image Contrast
- Adjust contrast to make text stand out from the background.
- This can help the OCR engine distinguish characters more effectively.
Step #5: De-skewing
- Correct image skew or rotation, ensuring text is horizontal.
- Skewed text can lead to misinterpretations by OCR.
Step #6: Choose the Right OCR Engine
- Different OCR engines have varying strengths and weaknesses.
- Experiment with multiple OCR engines to find one that suits your needs.
Why KlearStack Is the Best OCR Engine for Your Business?
If your company is processing 1000+ documents monthly, and want to achieve high OCR accuracy, then look no further!
KlearStack is your comprehensive OCR solution. It stands out with its high recognition accuracy, cost savings, and advanced AI integration, making it a powerful choice for efficient OCR solutions.
Here’s why KlearStack is the Best OCR Engine for Your Business:
- Allows training for specific fonts or languages,
- 99% recognition accuracy.
- Elimiates 70% costs
- Leaverages Machine Learning and AI for 95% adaptive recognition
- 200% increase in productivity levels.
- Instant set-up. No pre-requisite required
|Features||KlearStack||Traditional OCR Solutions|
|Training for Specific Fonts||Yes||Limited or Absent|
|Cost Reduction||Eliminates 70% Costs||May Not Offer Savings|
|Machine Learning & AI||95% Adaptive Recognition||Limited AI Integration|
|Productivity Increase||200% Increase||Standard Efficiency|
|Instant Set-up||Yes||Often Requires Setup|
|Prerequisites Required||None||May Have Requirements|
Take your OCR endeavors to the next level. Schedule a LIVE DEMO CALL and let KlearStack exceed your business expectations today!
What is the accuracy level of OCR?
The accuracy level of OCR varies but can reach up to 99% with advanced solutions like KlearStack. This means that they correctly transcribe 99 out of 100 characters or words from scanned images or documents, minimizing errors and enhancing reliability.
How do you evaluate OCR results?
To assess OCR results, you compare the extracted text to the original source (ground truth). Errors, including substitutions (incorrect characters/words), insertions (extra text), and deletions (missing text), are counted.
What is the F1 score in OCR?
The F1 score in OCR is a measure of the balance between precision and recall, providing a single metric for overall accuracy. Precision measures the proportion of true positive (correctly recognized) results among all positive results, while recall gauges the proportion of true positives among all actual positives.
What are the metrics for OCR error?
Metrics for OCR error assessment include accuracy, precision, recall, and F1 score, which collectively measure how well the OCR system performs in transcribing text from images.