ocronverter 0.1.0

Convert between OCR output formats via a neutral intermediate model.

ocronverter

Convert between OCR output formats through a single neutral intermediate model.

<provider data> --parse--> Document (neutral) --emit--> <provider data>

Instead of writing an N×N matrix of direct converters, every format has one parser (provider → Document) and one emitter (Document → provider). Any source can then be converted to any target.

Supported formats

Engine	Canonical name	Aliases	Data shape
Google Cloud Vision	google_vision	google, googlevision, vision, gcv	JSON
AWS Textract	textract	aws, aws_textract	JSON
Tesseract (image_to_data)	tesseract	tess, tsv	JSON dict / TSV
Azure AI Document Intelligence	azure	azure_di, document_intelligence, form_recognizer, di	JSON
hOCR	hocr	ocr_html	XHTML string
EasyOCR (readtext)	easyocr	easy_ocr, easy	list of detections

Format names are case-insensitive and -/_ interchangeable. ocronverter.list_formats() returns the canonical list at runtime.

Usage

import ocronverter

# Parse provider output into the neutral Document
doc = ocronverter.parse(data, "google_vision")

# Emit the neutral Document as another provider's format
out = ocronverter.emit(doc, "textract")

# Or do both in one step
out = ocronverter.convert(data, "google_vision", "textract")
out = ocronverter.convert(data, "gcv", "hocr", as_json=False)   # aliases OK

data may be a dict, a JSON string, a Python list (EasyOCR's native detections shape), or an hOCR/XHTML string. convert returns a dict (or a list for EasyOCR, or a str for hOCR); pass as_json=True for a JSON string where the target is JSON-based.

Line synthesis (split_lines)

Google Vision groups by paragraph and has no real line level, while line-oriented targets (Textract, Tesseract, Azure, hOCR, EasyOCR) read best with one entry per visual line. convert(..., split_lines=...) controls this:

• "auto" (default) — split only when going from a paragraph source to a line-oriented target.
• True / False — force on or off.

The neutral model

Document
 └─ Page[]        size + rotation; the coordinate anchor
     └─ Block[]   ~ paragraph / region
         └─ Line[]
             └─ Word[]
                 └─ Symbol[]   (optional; char/glyph level)

Conventions:

• Every level is optional. Parsers fill only what the source provides; the library never invents levels on parse. Missing levels are synthesized on demand via synth.py (opt-in).
• Geometry is stored normalized (0.0–1.0 of the page) as both an axis-aligned bbox and a polygon, kept in sync. Page.width/height/unit anchor denormalization back to provider pixels (or Azure inches).
• Confidence is always stored 0.0–1.0 internally; parsers rescale.
• provider_meta on every node carries source-specific fields verbatim, so a same-format round-trip is best-effort lossless.

Format-specific notes

• Tesseract / hOCR have a paragraph level the model lacks; it is carried on Line.provider_meta (par_num / par_id) and regrouped on emit.
• Azure uses flat per-page words[]/lines[] lists whose nesting is reconstructed from character span offsets, and may use inch page units.
• hOCR is an XHTML string: geometry and confidence live in each element's title attribute (bbox, x_wconf). Optional char-level ocrx_cinfo is not modeled (no glyph level).
• EasyOCR is a flat [polygon, text, confidence] list with no hierarchy and no page dimensions. Each detection becomes a Line + single Word; page dims are inferred from the max extent of all polygons (pass a {"width", "height", "results"} wrapper to supply true dims).

Tests

python -m pytest tests/

Each format has a round-trip test (provider → Document → provider) plus cross-format bridge tests. Tests are stdlib-only and need no OCR binaries, except test_real_tesseract, which runs only if the tesseract binary and PIL are installed.

License

MIT — see LICENSE.