High Precision Chinese English OCR Pro

Problem

Scanned PDFs, printed documents, and handwritten pages often expose brittle text extraction: mixed Chinese/English print, low-quality scans, broken handwriting, and formulas embedded in prose can produce rn/m, 0/O confusion, and lost heading, paragraph, or formula structure.

How It Works

The skill organizes the pipeline as preprocessing -> multi-engine OCR -> LLM correction. For PDF input, it first distinguishes digital PDFs from scanned pages and extracts embedded text when available; scanned pages are rasterized before OCR. Preprocessing is selected by scenario, such as English print, handwriting, or mixed Chinese/English content. The recognition layer can combine RapidOCR, EasyOCR, TrOCR, Pix2Text, and PP-OCR: lightweight local models handle common Chinese/English text, TrOCR is preferred for English handwriting, Pix2Text converts formula images to LaTeX, and formula-bearing documents separate text from formula regions. A vision model then reviews the original image and corrects the OCR output, returning txt, json, markdown, or latex.

Scope and Caveats

It fits English homework, math formulas, scanned textbooks, mixed-language papers, and table structure extraction. TrOCR is relatively large and requires model downloads on first use; formula recognition works best on clear formula images, and complex layouts still need human review. For high-accuracy tasks, cross-check multiple engines and keep the source image available for LLM correction.