I Tested 6 Python OCR Libraries on the Same Invoice (2026)

The Python OCR landscape has changed significantly since 2025. New contenders like RapidOCR and Surya have matured, LLM-based OCR (Qwen 2.5-VL, MiniCPM-o, Mistral OCR) is production-ready, and PaddleOCR keeps shipping updates. I tested the six most relevant open-source libraries on the same invoice to see which one you should actually use.

The Results

PaddleOCR and Surya benchmarks from prior controlled tests (same invoice, CPU). Tesseract, RapidOCR, and EasyOCR tested live on March 6, 2026. DocTR scores from community benchmarks on similar documents.

Accuracy measured as percentage of 24 known text items correctly extracted from test invoice.

Processing time for a single 800x600 invoice on Apple M-series CPU. Lower is better.

What Each Library Got Wrong

The pattern is clear: EasyOCR's dollar sign confusion is systematic and fatal for invoice/financial processing. RapidOCR struggles with word spacing. Tesseract has random punctuation issues. PaddleOCR and Surya get it right.

Feature Comparison

Scores out of 10 across five dimensions. No single library wins everything.

What Changed in 2025-2026

The OCR landscape shifted significantly. The biggest changes:

Which Library Should You Use?

Production Deployment Notes

Installation and Code

PaddleOCR

Install: pip install paddleocr paddlepaddle

from paddleocr import PaddleOCR

ocr = PaddleOCR(lang='en', use_angle_cls=True)
result = ocr.predict('invoice.png')
for item in result:
    for text in item.get('rec_texts', []):
        print(text)

Tesseract

Install: brew install tesseract && pip install pytesseract Pillow

import pytesseract
from PIL import Image

image = Image.open('invoice.png')
text = pytesseract.image_to_string(image)
print(text)

# With confidence scores
data = pytesseract.image_to_data(image, output_type=pytesseract.Output.DICT)
for i, word in enumerate(data['text']):
    if int(data['conf'][i]) > 60:
        print(f"{word} (conf: {data['conf'][i]}%)")

RapidOCR

Install: pip install rapidocr-onnxruntime

from rapidocr_onnxruntime import RapidOCR

engine = RapidOCR()
result, elapse = engine('invoice.png')
for bbox, text, conf in result:
    print(f"{text} ({float(conf):.2%})")

EasyOCR

Install: pip install easyocr (pulls ~1.5GB PyTorch)

import easyocr

reader = easyocr.Reader(['en'], gpu=False)
result = reader.readtext('invoice.png')
for bbox, text, conf in result:
    print(f"{text} ({conf:.2%})")

Surya

Install: pip install surya-ocr

from surya.recognition import RecognitionPredictor
from surya.detection import DetectionPredictor
from PIL import Image

det_predictor = DetectionPredictor()
rec_predictor = RecognitionPredictor()

image = Image.open('invoice.png')
predictions = rec_predictor([image], [["en"]], det_predictor)
for page in predictions:
    for line in page.text_lines:
        print(line.text)

DocTR

Install: pip install python-doctr[torch]

from doctr.io import DocumentFile
from doctr.models import ocr_predictor

model = ocr_predictor(pretrained=True)
doc = DocumentFile.from_images('invoice.png')
result = model(doc)
print(result.render())

The LLM Alternative

If accuracy on complex documents is all that matters and you have budget, skip traditional OCR entirely. Vision-language models like GPT-4o, Qwen 2.5-VL, and Mistral OCR handle tables, handwriting, mixed layouts, and even charts better than any library above.

The tradeoff: ~$0.01-0.03/page, 5-10s latency, and you need an API key or serious GPU for self-hosting. For batch processing 100K+ documents, traditional OCR is still the way.

Bottom Line

PaddleOCR remains the most accurate free option in 2026. Surya is the most exciting newcomer for layout-heavy documents. RapidOCR is the best lightweight choice. Tesseract is still unbeatable for speed on clean text. EasyOCR has fallen behind.

The real question in 2026 is no longer "which OCR library" but "do I even need a traditional OCR library?" For many use cases, a single API call to GPT-4o or Qwen 2.5-VL gives better results with zero setup. Traditional OCR still wins on cost, speed, and privacy (everything runs locally).

More

• Getting started with OCR in Python
• PaddleOCR vs Tesseract deep dive
• GPT-4o vs PaddleOCR deep dive