Optical Character Recognition (OCR) is fundamentally a fallback protocol. It is the process of applying machine learning models (like Tesseract or cloud Vision APIs) to rasterized images to probabilistically guess the underlying text.
The Error Matrix
OCR is never 100% accurate. Accuracy degrades exponentially when introduced to:
- Low DPI: Resolutions below 300 DPI lack the pixel density to differentiate similar glyphs (e.g., '1', 'l', 'I').
- Artifacting: JPEG compression artifacts introduce noise that confounds edge detection algorithms.
- Skew and Warp: Scanned documents requiring deskewing suffer from interpolation loss.
- Complex Backgrounds: Low contrast between the text matrix and the background canvas.
The Golden Rule of OCR
If you have access to the original vector text or structural source, never rasterize and OCR. It is computationally expensive and guarantees data loss.