Quick testing approach for evaluating handwriting recognition services with sample images from my Samsung A54.

The Testing Process

Simple three-step methodology for comparing handwriting recognition services:

1. Pick a Sample Image

Using the existing Termux workflow to access camera photos:

# Access latest handwritten sample
ls ~/storage/dcim/Camera/
cp ~/storage/dcim/Camera/$(ls -t ~/storage/dcim/Camera/ | head -1) sample-handwriting.jpg

2. Document Manual Upload Steps

For each online service tested, record:

• Upload method (web interface, API, mobile app)
• Image format requirements
• Authentication requirements

3. Report Back on Results

Compare services on:

• Accuracy - Character recognition rate
• Speed - Processing time
• Usability - Upload workflow complexity
• Cost - Per-image or subscription pricing
• Output format - Plain text, structured data, searchable PDF

Initial Services to Test

• Google Vision API
• Microsoft Azure Cognitive Services
• Amazon Textract
• Apple Live Text (iOS comparison)
• Open-source alternatives (Tesseract, PaddleOCR)

Results and detailed comparisons to follow in subsequent posts.

Update: October 2025

Google Vision API worked so well on the first attempt that I’m not going to test any others. See Google Vision Handwriting Recognition Works! for details on the successful implementation.