Super AI OCR-Pro v2.1: Chinese-English and Math Formula Recognition

Problem to Solve

Scanned documents, handwritten notes, formula screenshots, and paper PDFs often expose the limits of plain text extraction: characters break, formulas disappear, and mixed Chinese-English layouts lose structure. For engineering work, the issue is not just recognizing text, but producing reliable results across print, handwriting, and math notation while preserving position and format.

How It Works

The skill organizes OCR into three stages:
- Image preprocessing: uses standard, english, and handwriting modes for denoising, contrast, sharpening, and stroke repair.
- Multi-engine recognition: selects among RapidOCR, EasyOCR, TrOCR, Pix2Text, and PP-OCR variants; handwritten English can use TrOCR, while formula images can be converted to LaTeX.
- LLM correction: a vision model compares the raw OCR text against the source image and fixes common confusions such as rn/m, cl/d, and 0/O.

Digital PDFs are extracted directly when embedded text exists. Scanned PDFs are rasterized and then processed. Output can be plain text, JSON, Markdown, or LaTeX.

Boundaries and Notes

It fits Chinese scans, English textbooks, handwritten assignments, formula screenshots, and paper scans with equations, but it assumes a Python environment and network access for model downloads. TrOCR is large, and LLM correction adds API cost. Tables, low-quality scans, and mixed layouts may require preprocessing plus multi-engine verification instead of relying on one pass.