CodaraScan

CodaraScan is an offline Python package for locating and decoding barcodes in images and PDF documents. It combines two explicit engines behind one stable API: Tessera for low latency (mode="fast") and Mosaic for stronger recovery (mode="robust"). Results preserve quadrilateral geometry, localization-only and unresolved states, Unicode text, and original payload bytes.

0.1.0 is alpha software. APIs and schemas may change during 0.x. Pin an exact version and evaluate your own corpus before production use. All concrete formats readable by the installed ZXing-C++ release are selectable, but degraded-image validation depth varies by format. Stable 1.0 remains blocked on the production gates in release policy.

Install

CodaraScan supports CPython 3.11 through 3.14.

python -m pip install codarascan==0.1.0

PDF support is included in the normal installation through pypdfium2. No Poppler executable, server, network connection, runtime model download, or Codara application is required.

Python API

from codarascan import Scanner

scanner = Scanner(
    mode="fast",                 # Tessera; use "robust" for Mosaic
    symbols="all",               # "linear", "2d", or "all"
    formats=["qr-code", "code-128"],
    decode=True,
)
scanner.warm()                   # optional eager initialization

result = scanner.scan_image("shipping-label.png")
for symbol in result.symbols:
    print(symbol.status, symbol.quad)
    if hasattr(symbol, "text"):
        print(symbol.format, symbol.text, symbol.raw_bytes)

scan_image accepts image paths, encoded image bytes, Pillow images, 2-D uint8 grayscale arrays, and 3/4-channel uint8 BGR/BGRA arrays. EXIF orientation is applied when metadata exists. NumPy arrays are analyzed exactly as supplied and copied to isolate scans from caller mutation.

PDF scanning uses one-based page numbers and keeps requested order even with parallel analysis:

document = scanner.scan_document(
    "batch.pdf",
    pages=[3, 1, 2],
    workers=4,                    # default 1; positive integer or "auto"
    on_error="collect",           # default "raise"
)

with scanner.iter_document("large.pdf", workers=2) as pages:
    for page in pages:
        persist(page)
    print(pages.complete, pages.errors)

iter_document keeps at most the selected worker count in flight and does not accumulate returned pages. Both image and document calls accept an optional normalized (x, y, width, height) ROI and opt-in diagnostics.

One-off scan_image, scan_document, and iter_document functions reuse cached immutable scanner configurations. See the complete API contract.

Results

Stable symbol statuses are:

  • decoded
  • localized_unresolved_linear
  • localized_unresolved_matrix
  • localized when decoding is disabled
  • review_candidate

Decoded results expose text, its value alias, exact raw_bytes, canonical format, kind, confidence, sources, pixel quad, normalized quad, and analyzed dimensions. Localization-only results do not have payload or format attributes. Confidence is an engine-specific evidence score, not a probability and not directly comparable across engines; status is the semantic signal.

to_json(result) is deterministic, rejects non-finite numbers, and serializes raw bytes as Base64. Schemas and shared golden fixtures ship in codarascan/schemas for consumer contract tests.

Formats

groups = Scanner.supported_formats()
print([item.name for item in groups["1d"]])
print([item.name for item in groups["2d"]])

The capability is generated from the installed ZXing-C++ readable catalog and contains 27 linear and 13 matrix selections in the pinned 0.1.0 dependency range. It deliberately has no experimental/stability field. See compatibility and format status and the 0.1.0 format evidence.

CLI

codarascan image label.png --mode fast --symbols all --json
codarascan document dossier.pdf --pages 1-4,7 --workers auto --ndjson

Human output is the default. --json emits one shared-schema result; --ndjson streams page objects and a final document summary. Standard output is reserved for results. Warnings and debug-output locations use standard error. Exit codes are 0 (symbols), 1 (successful no-symbol scan), 2 (invalid usage/input), 3 (processing failure), and 4 (partial document result).

The private codarascan _worker command provides a persistent, versioned, length-prefixed JSON protocol for backend adapters without opening a network port. See worker protocol.

Native Tessera acceleration

Official wheels are expected to contain codarascan._sttg_native. Source installs build it when a supported compiler is available. If it cannot load, scanning continues through the Python reference backend and emits exactly one process-level warning:

CodaraScan could not load the native Tessera extension. Falling back to the slower Python implementation.

Set CODARASCAN_FORCE_PYTHON=1 to exercise the reference path. Result metadata records native or python-reference.

Offline, privacy, and resources

Import, warm, image scanning, PDF rendering, CLI, and worker operations make no network requests and emit no telemetry. Inputs, decoded payloads, rendered pages, diagnostics, and explicit debug output may be sensitive. Default scans leave no persistent crops, overlays, rendered pages, or payload files.

CodaraScan intentionally imposes no hidden limits on input bytes, dimensions, pages, workers, CPU, memory, or results. Large images, 300-DPI PDFs, broad format searches, diagnostics, and high worker counts can exhaust resources. Callers own quotas, isolation, timeouts, admission control, and cancellation.

Development

git submodule update --init --recursive
python3.11 -m venv .venv
.venv/bin/python -m pip install -e ".[dev]"
.venv/bin/ruff check src/codarascan tests
.venv/bin/mypy
.venv/bin/python -m pytest
.venv/bin/python -m build

Research history remains under archive/, benchmarks/, and the authored barcode report. Those assets are not installed as runtime package data. See contributing, migration notes, known limitations, and changelog.

CodaraScan is licensed under Apache-2.0. Third-party provenance is recorded in THIRD_PARTY_NOTICES.md.

Project links

Data verified by PyPI on Aug 9, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
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  • Released:
Newer release available (0.1.3)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for fellahiabderraouf from gravatar.com fellahiabderraouf

Credits

Author: Abderraouf FELLAHI

GitHub Statistics

Data verified by PyPI on Aug 9, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

Apache-2.0
View SPDX License List

Requires

Python <3.15, >=3.11

Provides Extra

test dev benchmark experiments

Tags

barcode qr-code data-matrix pdf zxing

Classifiers

Report project as malware

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