Best OCR for Handwritten Text

December 2025. Tested on meeting notes and prescription. Real results.

Handwriting OCR is where traditional OCR engines fail. Clean printed text is one thing. Cursive writing with varying slants, connected letters, and personal quirks is another.

I tested five OCR engines on two handwritten samples: meeting notes with numbers and a prescription. Including the new Mistral OCR 3 released December 2025.

Dec 2025 Update: Added Mistral OCR 3 (mistral-ocr-2512) which claims improved handwriting recognition with 7.1% CER on cursive text.

Test 1: Meeting Notes

Handwritten meeting notes with dates, dollar amounts, and action items

Handwritten notes. Contains "$45,000", dates, and bullet points.

Engine	Time	Confidence	Critical Errors
GPT-4o	3.75s	N/A	None - perfect
Mistral OCR 3 NEW	1.2s	92.9%	None - correct
PaddleOCR	3.47s	97.6%	Minor accent chars
Tesseract	0.54s	89.6%	"$45,000" became "445,000"
EasyOCR	0.50s	84.5%	"$45,000" became "#45,000"

The Critical Errors

Tesseract and EasyOCR both failed on the dollar amount:

Tesseract: "Dec" became "bec", "Q4" became "24", "$45,000" became "445,000"
EasyOCR: "Q4" became "@4", "$45,000" became "#45,000", fragmented output

These aren't minor errors. If you're processing meeting notes to extract action items or budget figures, "$45,000" becoming "445,000" or "#45,000" breaks everything.

Test 2: Prescription

Handwritten prescription with drug name, dosage, and quantity

Simulated prescription. Contains medication name, dosage, and refill count.

Engine	Time	Confidence	Critical Errors
GPT-4o	1.99s	N/A	None - perfect
Mistral OCR 3 NEW	1.1s	91.8%	"500mg" correct, minor spacing
PaddleOCR	1.96s	97.2%	"500mg" became "50omg"
Tesseract	0.38s	89.2%	"Qty" became "Gty", "Dr." became "Pr."
EasyOCR	0.30s	66.5%	"500mg" became "Soomg", "3x" became "zx"

EasyOCR's confidence dropped to 66.5% - it knows it's struggling. Mistral OCR 3 handled the prescription well, correctly reading "500mg" where PaddleOCR failed. And "500mg" becoming "Soomg" with EasyOCR is the kind of error that could cause medical harm.

Why GPT-4o and Mistral OCR 3 Win

GPT-4o achieved perfect transcription on both samples. Mistral OCR 3 came close with only minor spacing issues. Here's why they outperform traditional OCR:

Context understanding: They know "Q4" is a quarter, "$" precedes numbers, "mg" follows dosages
Language model: Can infer missing or unclear characters from context
Training data: Trained on diverse handwriting styles, not just printed text
Mistral's advantage: 3x faster than GPT-4o at 1/5th the cost for handwriting tasks

Traditional OCR engines detect shapes and match them to characters. Vision LLMs understand what the document is saying and fill in gaps intelligently.

My Recommendation

For critical handwriting (medical, legal, financial): Use GPT-4o. Perfect accuracy. The ~$0.01-0.02/image cost is negligible compared to errors.
For high-volume handwriting (cost-sensitive): Use Mistral OCR 3. $2/1000 pages. Near GPT-4o accuracy, 3x faster. Best value for handwriting in 2025.
For bulk processing (low stakes): PaddleOCR is acceptable. 97% confidence, but verify critical fields manually.
Avoid for handwriting: Tesseract and EasyOCR. They're built for printed text. Currency symbols, dates, and medical terms consistently fail.

The Code

GPT-4o (Recommended)

import base64
from openai import OpenAI

client = OpenAI()

with open('handwritten_note.png', 'rb') as f:
    img = base64.b64encode(f.read()).decode()

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": [
        {"type": "text", "text": "Transcribe all the handwritten text in this image exactly as written."},
        {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{img}"}}
    ]}]
)

print(response.choices[0].message.content)

Mistral OCR 3 (Best Value)

from mistralai import Mistral
import base64

client = Mistral(api_key="your-api-key")

with open("handwritten_note.png", "rb") as f:
    img_data = base64.b64encode(f.read()).decode()

response = client.ocr.process(
    model="mistral-ocr-2512",
    document={"type": "image", "data": img_data}
)

print(response.content)

PaddleOCR (Free Backup)

from paddleocr import PaddleOCR

ocr = PaddleOCR(lang='en')
result = ocr.predict('handwritten_note.png')

for item in result:
    for text in item.get('rec_texts', []):
        print(text)

Bottom Line

Handwriting OCR is a solved problem if you use GPT-4o or Mistral OCR 3. Traditional OCR engines were designed for printed text and struggle with cursive, connected letters, and handwriting quirks.

For critical documents (prescriptions, legal, financial) - use GPT-4o for perfect accuracy. For high-volume processing where cost matters - Mistral OCR 3 at $2/1000 pages is the best value. For low-stakes bulk processing, PaddleOCR can work with manual verification.

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