Why PaperEngine exists

Most tools answer one narrow question: "Is this text copied?" or "Does this look AI-written?". Rejection happens for dozens of other reasons — missing ethics statements, impossible statistics, unreferenced figures, template violations, predatory venue traps, retracted citations. PaperEngine runs 68 specialized engines against your manuscript and returns every finding as:

Severity | Finding | Evidence (quoted from your paper) | Confidence | How to fix it

The honesty principle (by design, not marketing): Similarity is not plagiarism. An AI-risk score is not proof of AI authorship. Every finding carries evidence + confidence, and the readiness score is informational — final judgment stays with humans, exactly how editors are trained to use iThenticate/Similarity Check.

How to use

Step 1 — Install (one time, under a minute)

Requirements: Python 3.10+. PDF support needs pypdf, image forensics needs Pillow — both come with the [all] extra.

Option A — straight from PyPI (simplest):

pip install paperengine[all]
papercheck --list-venues    # verify: prints all 19 venue presets

Option B — clone the source repo (development / latest changes):

git clone https://github.com/abnsr-sol/paperengine.git
cd paperengine
pip install -e .[all]       # editable install — every git pull is picked up automatically

Windows tip: if pip isn't on PATH, use py -m pip install paperengine[all].

No-install option: from the cloned folder, replace papercheck with python -m papercheck in any command below.

Step 2 — Use the Desktop version (web GUI)

papercheck --gui                 # launches the server and opens your browser
papercheck --gui --port 9000     # custom port if 8765 is already taken

Then, in the browser:

1. Drag your manuscript (.docx / .txt / .md / .tex / .pdf) onto the upload zone — or click to browse
2. Choose the standard — International (IEEE/Elsevier/ACM…) or National (India: UGC/AICTE/NAAC)
3. Pick the venue preset — e.g. ieee_conference, ugc_care, mdpi (the list filters by standard)
4. Click Check my paper → the full report renders in the browser: readiness score + findings table (severity · finding · evidence · confidence · how to fix)

Privacy / no retention: the GUI runs on your machine only (localhost). The file is parsed in memory, checked by the same 68 engines as the CLI, and the temporary copy is deleted the moment your report is rendered — every time, no exceptions. Nothing is stored, nothing leaves the machine.

💡 Before/after comparison is a CLI feature: papercheck v1.docx --compare v2.docx gives you fixed / still-open / new findings plus the score delta.

Step 3 — Use the CLI version

Basic pattern:

papercheck <file> [--standard international|national] [--venue <preset>] [--format <format>] [--out <file>]

Common tasks:

Without installing, run from the cloned folder with python -m papercheck … instead:

python -m papercheck sample_paper.txt --venue elsevier

# Full report to a file (console | markdown | html | fixplan | csv)
python -m papercheck paper.docx --venue mdpi --format html --out report.html

# Indian national standards (UGC/AICTE/NAAC)
python -m papercheck thesis.docx --standard national --venue ugc_care

# Prioritized fix plan (criticals first, effort-estimated)
python -m papercheck paper.docx --venue ieee_conference --format fixplan

# Batch-scan a folder, worst-first summary table or CSV
python -m papercheck --batch papers/ --venue ugc_care --format csv --out summary.csv

# Before/after revision comparison
python -m papercheck v1.docx --compare v2.docx --venue elsevier --format html --out diff.html

# With online lookups (Crossref): duplicate-publication + DOI validation
python -m papercheck paper.docx --venue springer --online --mailto you@university.edu

# Compare against your already-published papers (duplicate / "no new content")
python -m papercheck paper.docx --corpus ./my_prior_papers/

One-time retraction database (optional, recommended)

papercheck --update-rwdb
# caches 70k+ retraction records (CC-BY 4.0, Crossref) at
# %TEMP%/papercheck_rwdb.json (Linux/macOS: /tmp/papercheck_rwdb.json).
# From then on, retracted-reference screening runs against the full DB offline.

What the engines check

The full 48-angle rejection map (with engine-by-engine status) is in COVERAGE_MATRIX.md; every engine is listed with its exact checks in the architecture section below.

Venue presets (dual standard)

papercheck --list-venues

Every preset works in both the CLI and the web GUI; --venue-json rules.json accepts exact limits for any venue not yet preset.

Output formats

Every finding, in every format, carries: severity · finding · evidence · confidence · concrete action.

Architecture

papercheck/
├── __main__.py        CLI entry point (single file, batch, compare, gui modes)
├── ingestion.py       DOCX (stdlib zip+XML), TXT/MD/TeX, PDF (optional pypdf)
├── metrics.py         text statistics (readability, burstiness, n-grams, …)
├── venues.py          19 venue rule presets + --venue-json override
├── risk.py            Finding / Severity / RiskReport / readiness score
├── report.py          console, Markdown, and HTML renderers
├── fixplan.py         prioritized, effort-estimated fix-plan renderer
├── compare.py         before/after revision diff (fixed / still open / new)
├── batch.py           folder scanning, worst-first ranking, CSV writer
├── rwdb.py            Retraction Watch DB download/cache/screening
├── webui.py           local drag-and-drop GUI (stdlib http.server)
└── checks/            68 engines — one per rejection angle
    compliance · structure · language · citations · claims · ai_risk ·
    integrity · novelty · consistency · figures · forensics · policy ·
    statistics · overclaiming · self_plagiarism · citation_integrity ·
    reproducibility · submission · ugc_plagiarism · reference_verify ·
    fabrication · methodology · reporting_guidelines · writing_depth ·
    legal_ethics · citation_cartel · paper_mill · predatory_journal ·
    retracted_refs · submission_package · image_forensics · stats_deep ·
    design_claims · redundancy · domain_checklists · literature_search ·
    scope_match · rebuttal · crossref_verify · author_network ·
    reviewer_fraud · image_manipulation · llm_artifacts · supplementary ·
    data_license · abstract_quality · citation_age · sex_gender ·
    stats_plan · editorial_format · author_info · figure_quality ·
    venue_extras · ai_disclosure_deep · safety_ethics · authorship ·
    repro_env · paragraph_structure · transitions ·
    reference_completeness · funder_compliance · peer_review ·
    domain_checklists2 · grammar_tool (optional LanguageTool) · cross_check ·
    statcheck (p-value recomputation) · grim_engine (GRIM/GRIMMER) · ugc_14word (UGC 2018 clause-7/8)

Extending: add checks/my_angle.py with run(doc, ctx) -> [Finding], register it in checks/__init__.py, add tests. New venue: one dict in venues.PRESETS. See CONTRIBUTING.md for the ground rules (evidence + confidence + action on every finding, offline-first, dual standard).

Plug-in points already in the code: LanguageTool server (grammar_tool), Crossref/OpenAlex (--online), Retraction Watch DB (rwdb.py), AI-detector APIs (checks/ai_risk.py — documented hook, no verdicts).

Project layout

Development

python -m unittest discover -s tests        # 150+ tests, offline, no services needed
python scripts/maintenance.py               # tests + retraction-cache refresh

CI (.github/workflows/ci.yml) runs the full suite on Python 3.10 – 3.13 on every push and PR. A weekly scheduled job refreshes the retraction database and re-runs the suite. Tagging vX.Y.Z triggers the PyPI publish workflow (tag/version match is verified first).

To enable PyPI uploads: already done — the package lives at pypi.org/project/paperengine. Every future v* tag publishes automatically via the trusted publisher.

Honest limitations — and what we built to overcome them

Every tool has limits. Most hide them; we ship mitigations and measurement for ours:

Run the benchmark yourself: python scripts/benchmark.py (or --json for machine-readable output). It exits non-zero if calibration regresses.

License

MIT — free for research, commercial products, and institutions.