I Ran the Same Invoice Through PaddleOCR and Tesseract
December 2025. Real test, real numbers.
Tesseract has been around since the 1980s. PaddleOCR shipped in 2020. Both are free. Both are open source. The question is whether 35 years of development beats 5 years of deep learning.
The Test
Same invoice, both engines, measured everything.
Test invoice. 800x600 pixels, white background, standard fonts.
The Results
| Metric | PaddleOCR | Tesseract 5.5 |
|---|---|---|
| Time | 4.85s | 0.77s |
| Confidence | 99.6% | 91.1% |
| Character errors | 0 | 3 |
| Table structure | Lost | Partially preserved |
| Dependencies | ~500MB | ~10MB |
How They See the Document
The bounding boxes reveal why Tesseract is faster: it breaks text into smaller chunks and does less work understanding context.
PaddleOCR: 29 boxes, 99-100% confidence
Tesseract: 47 boxes, 75-96% confidence
Tesseract detected 47 text regions vs PaddleOCR's 29. More boxes means more opportunities for errors at word boundaries - which is exactly where Tesseract failed ("Qty" split into "ay", "UI/UX" became "UWUX").
Tesseract: Fast but Error-Prone
Tesseract finished in 0.77 seconds. Six times faster than PaddleOCR. But it made mistakes:
"Qty"became"ay""UI/UX Design"became"UWUX Design""Tax (8.5%):"became"Tax (8.5%)"(missing colon)
On a clean, computer-generated invoice. Real-world scans would be worse.
INVOICE
Invoice #: INV-2025-001
Date: December 16, 2025
Bill To:
John Smith
123 Main Street
Description ay Price Total
Web Development Services 40 $150.00 $6,000.00
UWUX Design 20 $125.00 $2,500.00
...Notice "ay" instead of "Qty" and "UWUX" instead of "UI/UX". These aren't rare edge cases - they happened on a pristine test image.
PaddleOCR: Slower but Perfect
PaddleOCR took 4.85 seconds. Every character was correct. 99.6% confidence.
INVOICE
Invoice #: INV-2025-001
Date: December 16, 2025
Bill To:
John Smith
123 Main Street
San Francisco, CA 94102
Description
Qty
Price
Total
Web Development Services
40
$150.00
$6,000.00
...The output is flat - table headers are separate lines - but every character is right.
When Speed Beats Accuracy
Tesseract's 6x speed advantage matters when you're processing millions of documents and can tolerate some errors. If you're extracting text for search indexing, "UWUX" is close enough to "UI/UX" that most searches will still work.
Tesseract is also tiny. No GPU needed. No deep learning frameworks. Runs on a Raspberry Pi. Install with brew install tesseract and you're done.
When Accuracy Beats Speed
PaddleOCR wins when errors cost money. If "Qty: ay" means your invoice parser breaks, or "UWUX Design" fails your data validation, the 4-second wait is worth it.
For financial documents, medical records, legal contracts - anywhere a character error creates a real problem - PaddleOCR is the safer choice.
The Code
PaddleOCR
from paddleocr import PaddleOCR
ocr = PaddleOCR(lang='en')
result = ocr.predict('invoice.png')
for item in result:
for text in item.get('rec_texts', []):
print(text)Tesseract
import pytesseract
from PIL import Image
image = Image.open('invoice.png')
text = pytesseract.image_to_string(image)
print(text)My Recommendation
Use PaddleOCR when: Accuracy matters. Financial documents. Data validation. Anything where errors cost money.
Use Tesseract when: Speed matters. Search indexing. Batch processing millions of documents. Resource-constrained environments.
For most production systems processing important documents, start with PaddleOCR. If you hit performance walls and can tolerate some errors, benchmark Tesseract on your specific documents.