List OCR engines and whether each is currently usable on this machine.

Returns JSON: for each engine -> {available, status}. Call this first to see which engines compare_engines will actually run.

OCR a single image file (PNG/JPG/TIFF/BMP).

Args: path: absolute path to the image. engine: 'auto' (RapidOCR), 'rapidocr', 'tesseract', or 'finereader'. lang: ISO 639-1 code ('en','de','fr','ro',...). Mapped per-engine. preprocess: if true, apply grayscale/denoise/deskew first (needs opencv).

Returns JSON: {engine, ok, text, mean_confidence, line_count, low_confidence_count, lines:[{text,confidence,bbox}], warnings}.

OCR a PDF by rasterizing pages (PyMuPDF) then running an engine per page.

Args: path: absolute path to the PDF. engine/lang: see ocr_image. pages: 'all' or a range like '1-3,5'. dpi: rasterization DPI (default 300; higher = slower, more accurate).

Returns JSON: {page_count, pages:[{page, ...ocr_image result...}], full_text}.

OCR many images. paths_or_glob is a glob (e.g. 'C:/scans/*.png') or a JSON list of absolute paths. Returns JSON: {count, results:[...]}.

Run ALL available engines on one image and compare them — the core accuracy tool when you have no ground truth.

Returns JSON: per-engine {text, mean_confidence, ok}, plus pairwise text similarity, average agreement, and a 'consensus_engine' (the one whose output best agrees with the others).

Score OCR output against a ground-truth text file (CER/WER).

Provide EITHER ocr_text (already-extracted text) OR ocr_path (an image/PDF to OCR now with engine). Compares against the UTF-8 text at ground_truth_path.

Returns JSON: {cer, wer, char_accuracy_pct, word_accuracy_pct, substitutions, deletions, insertions, hits}.