AutosXtract

Cascading text extraction from PDFs: every document descends steps from the cheapest to the most expensive and stops at the first one that produces acceptable text.

pip install autosxtract

from autosxtract import Cascade

r = Cascade().extract_file("document.pdf")
print(r.text)

That is it. One command and one class, identical on macOS, Linux and Windows — the OCR engine arrives already chosen by the machine, and there is no extra to memorise and no configuration to write.

Extraction does no networking. No external worker, no SSH tunnel, no remote endpoint, no third-party service call. The only connection in the whole package is the one-off download of the PP-OCRv6 weights — unnecessary once they are on disk.

---

Installation

pip evaluates platform markers on the installing machine, so the same pip install autosxtract brings different things on each system:

	macOS	Linux and Windows
OCR engine	Apple Vision, in-process	PP-OCRv6 tiny on ONNX
what pip brings	pyobjc-framework-Vision	rapidocr, onnxruntime
common to both	pymupdf, pydantic, numpy, pillow, opencv-python-headless, striprtf, asn1crypto	same
resulting cascade	native → vision	native → paddle
installed size	~330 MB	~570 MB

None of that needs to be known in order to use it. To check what your machine resolved:

autosxtract diagnose

On a Linux box with the default install the output is this, verbatim:

autosxtract 0.4.0
machine    Linux (x86_64)
resources  72 usable core(s)
automatic parallelism: 4 page(s) per document, 4 document(s) in flight (aggregate cap 144)

engines:
  [ ] vision       vision requires Darwin  (single queue: ignores threads)
  [ ] ocrmac       ocrmac requires Darwin  (single queue: ignores threads)
  [x] paddle       PP-OCRv6 tiny
  [ ] onnx         onnx unavailable: No module named 'onnxtr'; install with pip install autosxtract[onnx]
  [ ] tesseract    tesseract unavailable: No module named 'pytesseract'; install with pip install autosxtract[veto]

cascade:   native -> paddle

models:    /home/you/.cache/autosxtract  (complete)

On a Mac, vision is marked as well and the last line reads cascade: native -> vision -> paddle — PP-OCRv6 is not Vision's substitute there, it is the step BELOW it, and the second independent reading the agreement gate needs in order to fire at all. The other platforms' engines are the ones that then show why they are out. If you see cascade: native alone, no OCR engine was installed — the diagnosis says why and how to fix it, and it is the only case where a scanned PDF comes out empty.

Why Apple gets its own treatment

It is not a platform preference, it is a measurement. VNRecognizeTextRequest runs on-device on a small, specialised model with acceleration that has no x86 equivalent — 92% of the words and 100% of the numeric anchors preserved at the median of 60 audited documents, against 53% and 75% for the best local substitute. Where it exists, it is the right step.

And it runs inside your process: no server, no worker, no tunnel. A previous version of this pipeline reached Vision on a Mac over a reverse SSH tunnel, and that worker going down silently degraded the text — 488 documents re-extracted down the worse path and 28,239 characters lost without anyone noticing. That failure mode does not exist here.

The choice happens twice

when	who decides	what
at pip install	a platform marker in the package	what is installed
at the first extraction	platform.py plus the engine registry	what is available

Both are necessary. The second is what stops the library from blowing up when the marker did not match: a Docker image built on a Mac and run on Linux, an install with --no-deps, a lockfile generated for another system, an incomplete pyobjc. In any of those the cascade degrades with a warning instead of breaking.

Extras

None is needed for ordinary use. They exist to ask for the other engine — your platform's already arrived:

extra	what for
paddleocr	the three official PP-OCRv6 tiers, INT8 and a fine-tuned recogniser
paddle	nothing new — PP-OCRv6 is mandatory everywhere; kept so old commands work
apple	ocrmac, a safety net for Vision's direct path
veto	Tesseract as the third voice of the vetoes (needs the binary on PATH)
onnx	OnnxTR, a second cheap engine of a different architecture
remote	steps that talk to an external service (Docling API, vision model)
docling	Docling running inside the process, no network

pip install 'autosxtract[paddleocr]'      # to pick a model tier

PP-OCRv6 is not an extra: it installs on every platform, because on a Mac it is not Vision's substitute but the step below it — native -> vision -> paddle. That second engine is not redundancy either: it is what enables the agreement gate, which needs two independent readings to prove the reading is complete.

System requirements

• Python 3.11 or newer, on macOS, Linux or Windows.
• macOS: nothing beyond pip. Vision is part of the system.
• Linux/Windows: nothing beyond pip. opencv-python-headless avoids the graphics libraries the normal variant needs — which matters in a container.
• The veto extra: besides the Python package, the Tesseract binary on the PATH (apt install tesseract-ocr, brew install tesseract).
• First run off Apple hardware: it downloads ~10 MB of PP-OCRv6 weights into ~/.cache/autosxtract. After that the machine can stay offline. For a closed environment, run autosxtract download-models on a machine with network and point AUTOSXTRACT_MODELS at the copied directory.

---

In thirty seconds

from autosxtract import Cascade

cascade = Cascade()          # instantiate ONCE and reuse
r = cascade.extract_file("document.pdf")

r.text          # the extracted text
r.step          # who produced it: 'native', 'vision', 'paddle', ...
r.score         # 0.0 to 1.0
r.provenance    # "vision: native(no text layer) -> vision(ok)"
r.to_dict()     # all of that, ready for JSON

Instantiating Cascade() once and reusing it matters: loading the models is the dominant cost of the first call, and one cascade per document pays it on every file.

Several files at once, with parallelism already bounded by the machine:

results = cascade.extract_batch(["a.pdf", "b.pdf", "c.pdf"])   # {name: Result}

And from the terminal:

autosxtract diagnose              # what steps this machine has
autosxtract extract document.pdf  # text on stdout, provenance on stderr
autosxtract extract *.pdf --json output.json

Why a cascade, and not a single model

The temptation is to use one good model for everything. Measured across two real archives, it worsens the time:

	time
a single model at 2.5 pages/s, 935 documents	6.2 min
the cascade	4.44 min

The reason is the distribution, not the model: 31% of the documents already have a text layer and cost 13 ms. A single model would charge ~400 ms on pages that have no OCR to do.

    STEP                        COST/DOC   RESOLVES   QUALITY (60 audited docs)
    -------------------------------------------------------------------------------
    0. unwrap                    ~0.1 ms      —       RTF / BRy / PKCS#7
    1. native (PyMuPDF)          13.4 ms     31%      native text, exact
    2. Apple Vision             ~400 ms      64%      words 92%  anchors 100%
       or PP-OCRv6 tiny         ~500 ms               (off Apple hardware)
    3. vetoes + screening         ~1 s       the rest
    4. remote steps            4 to 47 s     opt-in   only if you instantiate them

Step 0 almost never does anything — on a real PDF it costs reading 16 bytes. It exists for the cases where the extension lies: in a real archive, 128 documents arrive as .pdf while being RTF, a BRy envelope or PKCS#7, and PyMuPDF raises Failed to open stream on them. No OCR recovers them — there is no image to recognise, there is plain text nobody was reading.

The gates

What stops the cascade from spending the expensive step for nothing. Each answers a different question, and every one of them exists because a measurement asked for it.

gate	question	why
stamp	is what is left content, or just the conformity banner?	in an audit of 1,339 documents, 403 (30%) had text that looked fine; in 227 of them there was only the court stamp — 250 to 600 characters that sail past any size threshold
coverage	does the text layer cover the sheet?	in a filing that embeds an official letter as an image, the native text is flawless and the attachment is never read
agreement	did two engines read the same thing?	it proves the reading is complete, not that the page is short. It costs nothing: both readings are already in hand
consensus	did all of them read almost nothing?	proof of absence. One dissenting engine is enough to escalate
contest	who wins in the end?	the highest quality × log(1+volume), not the last one to run

Before any step marked expensive = True, five more:

veto	question	why
photograph	is the page a photo rather than a document?	continuous tone with no text — the expensive step would only return [SIGNATURE]
no ink	is there ink on the sheet, outside the stamp?	calibrated at 1%: it avoids 2 of the 11 useless escalations without losing any of the 9 useful ones
no legible word	can a local OCR read anything here?	the only one that measures instead of estimating. It saves 27.2 minutes across 19 documents, and the largest content lost is a 124-character stamp
sparse page	is there anything to read?	"NOTICE OF INSPECTION", a signature sheet. Four of them paid for the expensive step to yield 126 to 433 characters
reading confirmed	have the two already read the same?	the agreement gate above, applied to the expensive step

Not escalating is not discarding: in all five cases the document keeps the text the cheap layer already read.

After an expensive step runs, the replacement gate decides whether it may take the previous text's place. Length alone gets both of the costliest cases wrong — partial coverage (a power of attorney transcribed up to page 10 of 15 is still longer than a bad extraction of all 15) and digit corruption (9XXYZ3ZE... scores exactly like 9XXYZ32E...). A candidate failed here is discarded, not demoted: volume is usually on the wrong side.

Provenance

Every result carries the whole path, with the reason for each refusal.

r = cascade.extract_file("document.pdf")

print(r.provenance)
# vision: native(quality 0.42 below 0.75) -> vision(ok)

for a in r.attempts:
    print(a.step, a.accepted, a.reason, a.chars, f"{a.ms:.0f}ms")

r.to_dict()   # ready for JSON, a log or a database

"The system extracted the text" is not an auditable answer. "The native step read 41 characters and was refused on density; Vision read 3,812 and passed" is.

Configuration

Every threshold in one place, each annotated with the measurement that fixed it.

from autosxtract import Cascade, Config

cascade = Cascade(Config(
    dpi=150,                # at 100 DPI anchor preservation falls to 85.5%
    min_useful_words=12,    # floor outside the stamp
    min_agreement=0.60,     # calibrated on 24 real escalations
    engines=["paddle"],     # explicit order; None = the machine decides
))

Parallelism

The same library runs on a 2-core laptop and a 72-core server, so the three parallelism fields accept None — decide from the machine — and that is the default.

Config(
    page_parallelism=None,      # None = min(4, cores)
    document_parallelism=None,  # None = min(4, cores)
    concurrency_cap=None,       # None = cores × 2; 0 turns it off
)

autosxtract diagnose shows what your machine resolved:

resources  2 usable core(s); affinity limits to 2 of 72
automatic parallelism: 2 page(s) per document, 2 document(s) in flight (aggregate cap 4)

Detection is the most restrictive of three sources, because none covers everything: sched_getaffinity (catches taskset and cpusets), the cgroup quota (catches docker run --cpus=2) and os.cpu_count(). os.cpu_count() lies inside a container — it reports the host's cores, so a pod with 2 CPUs on a 72-core machine would open 72 threads to fight over 2.

Why those defaults

Measured on PP-OCRv6 tiny, 12 real pages, the same machine restricted with taskset:

threads	72 cores	2 cores
1	1.36 pages/s	1.44 pages/s
2	1.74	1.68
4	1.99	1.58
8	2.18	1.54
16	2.27	1.71

On 2 cores throughput plateaus at 2 threads; asking for 8 delivers less (1.54 against 1.68) with 4× more pages in flight. On 72, going from 4 to 16 threads gains 1.14×. One fixed number serves both badly.

An explicit number is obeyed

Whoever knows what they are doing decides. What it is not, is a promise — the curve flattens early. Two caveats:

• The aggregate cap still cuts the batch. documents × pages grows unnoticed: 4 × 8 is 32 simultaneous pages, each holding a rendered image and the model's activations, and on a small machine the memory limit arrives before the CPU one. The cut lands on the pages, never on the documents — reducing documents raises total time predictably, reducing pages per document costs almost nothing. Turn it off with concurrency_cap=0.
• A single-queue engine ignores the number. Apple's Neural Engine serves one request at a time: measured from 1 to 12 threads, constant throughput at ~2.5 pages/s and latency from 430 ms to 3,492 ms. VisionEngine declares scales_with_threads = False and the cascade uses 1, recording the effective value in the provenance so nobody configures 8 and measures the time of 1 without understanding why. There the useful parallelism is per document.

Resolution is a method (config.pages_in_flight()), not a computed field: a preset stored in YAML and used in two environments has to answer differently in each.

Swapping the OCR model

The default is PP-OCRv6 tiny, and the choice is measured: with the bottleneck in CPU, what decides is throughput and not benchmark accuracy. But a default is a default — a different archive wants a different model, and swapping it does not require touching the cascade.

from autosxtract import Cascade, NativeStep, OCRStep
from autosxtract.engines.paddle import PaddleEngine

PaddleEngine()                              # PP-OCRv6 tiny (default)
PaddleEngine(det="tiny", rec="medium")      # small detector + large recogniser [paddleocr]
PaddleEngine(rec_dir="/my/finetuned_rec")   # your model, trained on your archive
PaddleEngine(quantized=True)                # INT8, if you exported it
PaddleEngine(preprocess="otsu")             # binarise before reading
PaddleEngine(providers=["CoreMLExecutionProvider", "CPUExecutionProvider"])

cascade = Cascade(steps=[NativeStep(), OCRStep(PaddleEngine(rec="medium"))])

Why det and rec are separate: accuracy lives in the recogniser. Finding where there is text is an easier task than reading what is written, so "small detector with large recogniser" tends to be the best-returning trade — and it only exists if the two are distinct parameters.

The engine picks its backend on its own: paddleocr when installed (giving the three official tiers, INT8 and a fine-tuned recogniser), rapidocr as the lightweight alternative — which serves the tiny and nothing else. To pick a tier, install pip install 'autosxtract[paddleocr]'. For an entirely custom engine, see the next section.

From the command line, to measure a swap without writing code:

autosxtract extract document.pdf --det tiny --rec medium
autosxtract extract document.pdf --rec-dir /my/finetune --no-layers

Containment layers

A small OCR engine reads the body of a document almost perfectly. The error concentrates in four places: vertical stamps, signatures over printed text, two-column headers and run-together words. No OCR reads through a stamp — so the strategy is not "read better", it is to contain the damage, flag where it is, and recover what is recoverable.

layer	what it does	cost
1	classifies each line; junk becomes [illegible], fragments are dropped, the body passes untouched	~2 ms, no model
1b	re-segments run-together words against the lexicon	included
1.5	visual signature detector (optional, see the caveat)	~35 ms/page
2	re-reads the targets: rotates the vertical stamp 90°, crops the dirty line tighter	~30 ms
3	per-page report: how much is trustworthy and what to do	free

Measured on 895 pages against the same engine without the layers: median CER 0.132 → 0.129, entity recall 0.902 → 0.921, p50 latency 298 → 236 ms. 79 pages improve, 4 get worse (all already bad), clean pages are untouched. On the vertical-stamp subset, recall rises from 0.745 to 0.837.

The principle behind the thresholds: illegible only for unambiguous junk. Anything doubtful becomes suspect — the text passes, but the page loses confidence and tends to escalate. That way the weak signal is not lost.

The report enters the provenance:

r = cascade.extract_file("document.pdf")
r.details["layers"]
# {'lines_total': 103, 'lines_illegible': 1, 'lines_vertical': 3,
#  'lines_recovered': 1, 'trusted_fraction': 0.975,
#  'needs_escalation': False, 'suggested_action': 'accept_with_holes'}

The layers need an engine that exposes line geometry (read_page). An engine that only returns running text keeps working — the layers simply do not run, and the provenance says so.

The lexicon decides what counts as the language

Classification compares the line's tokens against a lexicon, and its quality decides the classification's quality. The built-in one is a floor; building your own from validated texts is measurably better:

from autosxtract import Config
from autosxtract.quality.lexicon import Lexicon

mine = Lexicon.from_texts(Path("validated").glob("*.txt"))
Config(lexicon=mine)

With a small lexicon, more correct text falls into suspect — the safe side of the error, but it escalates pages for nothing.

The signature detector: read before switching it on

Off by default, and not out of generic caution. A yolo11n trained on a public signature dataset (mAP50 0.995 on the public validation set) did not transfer: across 895 pages of a legal archive it produced a detection on 19% of them, most of which were false positives — an authenticity seal at 0.92 confidence, a "DELIVERED" stamp, an ICP-Brasil logo, a QR code, a coat of arms, and the printed word "SIGNATURES". And it missed the target case: a cursive signature over a name produced no box even at 0.12 confidence.

The public dataset is contract signatures — thick strokes, isolated, clean background. The real case is a thin cursive scribble over text, on a degraded scan.

That is why a box never decides on its own: it only counts if some overlapping line is illegible, and it is discarded if any overlapping line is stamp text. That filter is what makes an imperfect detector usable.

Config(signature_detector="/my/models/sig.onnx")

For a stage 2 that works, annotate 200-400 pages of your archive, including NEGATIVES (stamps, seals, logos and QR codes as not-a-signature). Without them the model repeats the same false positives. The structural rule — a scribble above a name with a job title nearby — always runs, with or without a detector, and costs nothing.

Page routing

Classifies the page from what the cheap engine read, running no model (~1 ms): table, stamped_digital, degraded or normal. Switch it on with Config(page_routing=True); it does not change the extraction, it is a signal for whoever consumes it.

About tables, a measured verdict worth more than the route. Across 17 pages classified as tables, the cheap engine with layers recovered 0.797 of the numeric values against 0.699 for a dedicated structure model (SLANet), which also cost 6-29 s per page against 0.27 s. On a poor scan the bottleneck is not the structure model, it is the cell OCR — rebuilding the grid does not help if the recogniser cannot read the cell, and the rebuild still loses the surrounding prose. Across 895 pages, switching to the table model's output won on 1 page.

That is why the library classifies the route and does not embed a table step.

How to add an engine

A class with one method, plus the decorator. The cascade finds it on its own — nothing else changes.

from autosxtract.engines.base import OCREngine, register

@register(name="my_ocr", priority=25, extra="my-ocr")
class MyOCR(OCREngine):
    def _load(self):
        import my_ocr
        return my_ocr.Reader()

    def transcribe_page(self, image: bytes) -> tuple[str, float]:
        r = self.model.read(image)
        return r.text, r.confidence * 100

priority is preference order (lowest first). The defaults come from comparative measurement on the same sample: 10 Vision, 20 PP-OCRv6, 90 Tesseract.

Two rules the contract imposes:

• Confidence does not arbitrate quality. Across 60 audited documents it did not separate a good reading from an unsafe one — there was an unsafe document at 100. It enters only as a floor against degenerate output.
• A missing engine is never an exception. available() returns the reason in words, the step goes inert and the cascade moves on. The absence of a tool is not evidence about the document.

If your backend exposes line geometry, implement read_page as well: that is what enables the containment layers.

Remote steps

Docling and vision models exist in the library, but only through explicit instantiation. They are not in the default cascade, there is no environment-variable discovery, and Config has not a single host, URL or credential field. Whoever wants them builds them:

import os
from autosxtract import Cascade, NativeStep, OCRStep, ScreeningStep, get
from autosxtract.steps.remote import DoclingStep, VLMStep

cascade = Cascade(steps=[
    NativeStep(),
    OCRStep(get("vision")),
    DoclingStep(
        url="http://docling:5001",
        token=os.environ["DOCLING_TOKEN"],
        conversion_timeout=180,
        force_ocr_if_empty=True,
    ),
    ScreeningStep(),
    VLMStep(
        url="https://my-endpoint/v1",
        model="PaddleOCR-VL-0.9B",
        token=os.environ["VLM_TOKEN"],
        dpi=200,
        images_per_batch=2,
        max_tokens_per_page=2000,
        parallelism=8,
    ),
])

pip install 'autosxtract[remote]' brings httpx. Installing turns nothing on — the steps only exist if you instantiate them. Both are expensive = True, so the five vetoes run before and the replacement gate after. A network failure becomes a refused attempt with the reason in the provenance, never an exception. The token never appears in repr, in a log or in the result.

About the expensive step's model: a 27B model transcribing a sheet of paper is measured waste — 47.4 s per document. Specialised OCR models in the 0.5-3B range (GLM-OCR 0.9B, PaddleOCR-VL 0.9B, dots.ocr 1.7B, DeepSeek-OCR 3B, GOT-OCR2.0 0.58B) are built for this case and promise ~100×. That is why model is a parameter, not a constant.

Docling without a network

The same engine, running inside the process. The choice between the two forms is not obvious:

form	network	models	when to choose
DoclingStep	yes	on the server	several processes share one installation; the client stays light
LocalDoclingStep	no	~2 GB here	an isolated machine with no egress, or a single process

pip install 'autosxtract[docling]'

from autosxtract.steps.docling_local import LocalDoclingStep

docling = LocalDoclingStep(workers=2, ocr_engine="rapidocr")   # once
cascade = Cascade(steps=[NativeStep(), OCRStep(get("paddle")), docling])

Loading is lazy — building the step loads no model, so assembling a cascade and finding the PDF is native does not cost the 2 GB for nothing. But reuse the instance: one step per document pays the whole load on every file. Internally there is a pool of converters, because DocumentConverter is not thread-safe.

How to add a step

The other extension point. A step is any object with name and run(ctx) -> StepResult:

from autosxtract import Cascade, NativeStep, OCRStep, get
from autosxtract.steps.base import StepResult
from autosxtract.types import Attempt, Candidate

class MyFormatStep:
    name = "my_format"

    def run(self, ctx) -> StepResult:
        text = unwrap_my_format(ctx.pdf_bytes)
        if not text:
            return StepResult(Attempt(self.name, False, "not my format"))
        return StepResult(
            Attempt(self.name, True, "unwrapped", len(text)),
            Candidate(self.name, text, score=1.0),
        )

cascade = Cascade(steps=[MyFormatStep(), NativeStep(), OCRStep(get("paddle"))])

StepResult separates two things that are not the same: the verdict (does the cascade stop?) and the candidate (does the text enter the contest?). A refused step may still have produced the best reading the document has.

Declaring expensive = True makes the cascade run the five vetoes before it and submit the result to the replacement gate afterwards.

Architecture

autosxtract/
  cascade.py        the orchestrator — steps, gates, vetoes, contest
  config.py         every threshold, each with the measurement that fixed it
  formats.py        RTF / BRy / PKCS#7 — is the file really a PDF?
  types.py          Line, Page, Transcription, Candidate, Attempt, Result
  platform.py       Apple or not; the only hardware-dependent decision
  resources.py      how many cores there really are (affinity, cgroup, cpu_count)
  image.py          image dimensions from the header, with no dependency
  pdf/              knows only the file: lock, render, profile, coverage, ink
  quality/          knows only text: stamp, metrics, gate, consensus, anchors,
                    prose, screening, markers, vetoes, rejection, lines,
                    lexicon, routing
  engines/          what reads pixels: vision, paddle, onnx, tesseract,
                    signature (YOLO) + the registry
  steps/            native, generic OCR, unwrap, screening, layers,
                    docling_local (no network) and remote/ (explicit url)

The layers do not cross each other: pdf/ does not know what an engine is, quality/ does no I/O, engines/ does not know what a cascade is. That is what lets each be tested alone — and what makes a new engine cost one file.

What has been measured and does not work

This section exists so nobody spends time again on what has already been refuted.

tried	result
turning off Vision's language correction	doubles throughput (2.5 → 5.4 pages/s) and loses text: 102 documents fall to worse engines, −227 anchors and −4,981 characters across 935 documents
deduplicating pages	only 2.0% repeat (33 of 1,675)
predicting which documents will escalate (bytes/page)	medians of 89k against 109k, almost complete overlap
nine families of pixel statistics for "empty page"	an empty page and a dense-but-faded page produce identical statistics — what separates them is consensus between engines
Apple Vision's .fast mode	rejected on quality
grey JPEG at 100 DPI	85.5% anchor preservation; it loses dates and tax numbers
more threads against Vision	constant throughput, linear latency: it is a single hardware queue
collapsing the cascade into one model	6.2 min against 4.44 min
a dedicated table structure model	0.699 value recall against 0.797 for the cheap engine with layers, at 6-29 s/page against 0.27 s
a signature detector trained on a public dataset	19% of pages with a detection, most false positives on seals, stamps, logos and QR codes

Known limits

• Apple Vision does not scale with threads. A single Neural Engine queue: the ceiling is ~2.5 pages/s per machine. The library knows this and uses 1 thread there regardless of what you configure. The useful parallelism is per document.
• With no OCR engine installed, a scanned PDF comes out empty — correctly, and autosxtract diagnose says so in large letters. It happens when the platform marker did not match: an image built on a Mac and run on Linux, --no-deps, or a lockfile generated for another sys_platform.
• PyMuPDF is serialised by a process lock: it crashes the process under concurrency (segfault in page_get_textpage). The measured cost of serialising is ~4%.

Development

make hooks       # pre-commit install (do this first)
make test        # pytest
make lint        # ruff check + ruff format --check
make typecheck   # mypy
make privacy     # scan for sensitive artefacts
make all

pre-commit runs the privacy scan before the style hooks, and the order is deliberate: a commit blocked on formatting costs thirty seconds; a real document published has no undo. The scan validates Brazilian tax IDs, company IDs and case numbers by their check digit, not merely by their shape — a scanner that shouts at every 14-digit number is switched off in the first week.

It has already paid for itself: it caught a real case number that had made its way into this library's own examples.

Licence

MIT.