Public record
Software health reportschema 0.31.0 · metrics 2.10.0 · 2026-08-06 19:18 UTC

qurator-spk / eynollah

Document Layout Analysis

PythonApache-2.0★ 410 stars⑂ 38 forkssince Nov 2020View on GitHub ↗
KindCommand-line toolLibraryhow this is determined

qurator-spk/eynollah holds a health index of 73 out of 100, placing it in the Good band. It scores highest on Vitality (89/100) and lowest on Security (21/100). It was last updated 9 days ago. 2 contributors account for most of its recent work.

73
overall / 100
Good

Software health index

Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean, calibrated against the distribution of the public record so bands carry percentile meaning; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At Risk ceiling of 34.

73
Exceptional93-100The record's top tier (≈ top 5%); essentially all checked criteria met
Excellent80-92Strong across the board; minor gaps
Good65-79Healthy; gaps are limited and manageable
Moderate50-64Acceptable with notable gaps; review recommended
Weak35-49Material weaknesses across several areas
At Risk20-34Significant weaknesses; adoption warrants caution
Critical1-19Severe problems (abandoned, single-maintainer, no hygiene)
VitalityCommunity &AdoptionSustainability &GovernanceEngineeringQualitySecurityAI Readiness

Score profile

Each axis is a category. The shape matters more than the average — a healthy subject fills the whole shape, while a spike-and-crater profile means strength in one dimension is masking risk in another.

The weighted overall 64 is calibrated to 73 on the published index scale (record calibration 2026-08-02).

Ownership

IDM 4 Data ScienceOrganization
47 followers43 public repossince Jul 2019

This repository is backed by an organization — shared, accountable stewardship that can outlive any single maintainer.

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIeynollah0.9.2-239 days agodocument-layout-analysisimage-segmentationbinarizationoptical-character-recognition

Metrics by category

Vitality

Is the project alive — is code being written and are releases shipping?

89Excellent · 21% of overall
How it's scored
28.8/36Push recencylast push 9 days ago
31.2/36Commit cadence45/52 weeks with commits
18/18Commit volume748 commits in the last year
0/10OpenSSF Scorecard: Maintainedno data
Inputs used
commits_last_year748
human_commit_share1
days_since_last_push9
active_weeks_last_year45
How it's scored
27/27Ships releases25 releases published
36/36Release recencylatest release 9 days ago
19.8/27Release cadencea release every ~53 days
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count25
latest_release_tagv0.9.2
releases_from_tagsno
days_since_latest_release9
mean_days_between_releases53

Community & Adoption

Does the project have users, downloads, attention, and a welcoming setup for contributors?

56Moderate · 17% of overall
How it's scored
42.4/60Stars410 stars
13.1/25Forks38 forks
7/15Watchers19 watchers
Inputs used
forks38
stars410
watchers19
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5Licenserecognized license (Apache-2.0)
0/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges6
has_contributingno
has_issue_templateno
has_code_of_conductno
readme_badge_servicesgithub.com, shields.io
has_pull_request_templateno

Sustainability & Governance

Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?

78Good · 23% of overall
How it's scored
25.2/54Bus factor2 contributor(s) cover half of all commits
12.6/22.5Commit distributiontop contributor authored 44% of commits
13.5/13.5Contributor breadth10 contributors
0/10OpenSSF Scorecard: Contributorsno data
Inputs used
bus_factor2
contributors_sampled10
top_contributor_share0.442
How it's scored
31.4/42Issue resolution75% of issues closed
24.8/30PR acceptance91/110 decided PRs merged
13/13Newcomer PR acceptance1/1 first-time contributors' PRs merged in 30d
0/15OpenSSF Scorecard: Code-Reviewno data
Inputs used
merged_prs91
open_issues28
closed_issues83
prs_merged_7d0
prs_decided_7d0
prs_merged_30d2
prs_decided_30d4
issue_closed_ratio0.748
closed_unmerged_prs19
first_time_authors_30d1
first_time_prs_merged_30d1
first_time_prs_decided_30d1
How it's scored
30/30Ownership backingorganization-owned
0/20Verified domainverified-domain status not read for this organization
12.1/25Owner reach47 followers of qurator-spk
24/25Track record43 public repos, account ~7 yr old
Inputs used
followers47
owner_typeOrganization
is_verified
owner_loginqurator-spk
public_repos43
account_age_days2,563
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable1 package(s) on pypi
35/35Publish recencylatest publish 9 days ago
20/20Version history23 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packageseynollah
ecosystemspypi
any_deprecatedno
min_days_since_publish9

Engineering Quality

Are baseline engineering and documentation practices in place?

70Good · 19% of overall
How it's scored
24/24CI workflows3 workflow(s)
24/24Tests present
0/16Linter config
0/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Testsno data
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configno
has_precommit_configno

Documentation

85Excellent
How it's scored
30/30README
25/25Documentation directory
0/15Documentation / homepage site
10/10Repository description
10/10Topics5 topics
10/10Wiki
Inputs used
topicssegmentation, document-layout-analysis, ocr, textline-detection, binarization
has_wikiyes
homepage
docs_site
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

Are visible security and supply-chain practices strong, without unresolved high-risk jurisdiction exposure?

21At Risk · 16% of overall
How it's scored
0/30Security policy (SECURITY.md)
0/25Dependabot config
0/25Dependency lockfiles
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfiles
manifestspyproject.toml, requirements-ocr.txt, requirements-plotting.txt, requirements-test.txt, requirements-training.txt, requirements.txt, train/requirements.txt
has_codeql_workflowno
has_security_policyno
has_dependabot_configno

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisoriesno direct dependency carries a known advisory
25/25Indirect dependencies free of known advisoriesno indirect dependency carries a known advisory
0/40No advisories left outstandingno advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages99
unassessed_packages0
affected_by_severitynone
direct_affected_packages0
Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:eynollah@0.9.2 runtime dependency closure — what installing the published package pulls in — 99 packages. Reachability is not analyzed.

AI Readiness

How well is the repo equipped to be developed and maintained with AI coding agents? Carries a deliberately small weight (4%): agent tooling is a real maintenance signal, but a repository with none can still reach 100/100.

42Weak · 4% of overall
How it's scored
0/45Agent instructionsno CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
17.6/40Legible commit history33 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share0.33
agent_instruction_files
agent_instruction_max_bytes
How it's scored
18/18One-command bootstrapMakefile, models/Makefile
22/22Automated tests
0/11Lint / format config
0/11Static type checking
10/10Reproducible environmentDockerfile
0/10Demonstrated agent practiceno agent-authored commits among the last 100
0/8Automated maintenanceno automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependenciesno data
Inputs used
has_nixno
has_testsyes
lockfiles
has_dockerfileyes
typed_languageno
bootstrap_filesMakefile, models/Makefile
has_devcontainerno
has_linter_configno
typecheck_configs
agent_commit_share0
toolchain_manifests
dependency_bot_commit_share0
How it's scored
0/45Type-checkable codePython without a type-check config
51.9/55Manageable file sizes4/70 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes113,858
source_files_sampled70
oversized_source_files4

Key facts

410GitHub stars
10contributors
748commits, last 12 months
9days since last push
25releases
2bus factor
28open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • OpenSSF Scorecard did not return a usable result (2026/08/06 19:17:27 Warning: PATs stored in env variables GITHUB_AUTH_TOKEN and GITHUB_TOKEN differ. Scorecard will use the former.); skipping Scorecard checks

More detail

Star and fork history 0 ★ / 38 ⇿
0Stars
38Forks
25Releases

When each star and fork was added, collected from GitHub and bucketed by day. Cumulative growth sits directly above the daily additions it is made of, so the two read against each other: steady organic accretion looks nothing like an abrupt, short-lived burst. Where that difference is measurable, it is reported as growth authenticity.

0102030403822020-122023-102026-08
Major 0Minor 9Patch 14

Each point covers 6 days.

All dependencies 24

Full resolved dependency set from the GitHub dependency graph: 0 direct and 24 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIblackindirect
PyPIcoverageindirect
PyPIimutilsindirect
PyPImatplotlibindirect
PyPInumbaindirect
PyPInumpyindirect
PyPIocrdindirect
PyPIocrd-fork-sacredindirect
PyPIprotobufindirect
PyPIpytestindirect
PyPIpytest-isolateindirect
PyPIscikit-imageindirect
PyPIscikit-learnindirect
PyPIscipyindirect
PyPIseabornindirect
PyPIsetuptoolsindirect
PyPItabulateindirect
PyPItensorflowindirect
PyPItensorflow-addonsindirect
PyPItf-dataindirect
PyPItf-kerasindirect
PyPItorchindirect
PyPItqdmindirect
PyPItransformersindirect
Dependency advisories 0

Installing pypi:eynollah@0.9.2 pulls in 99 packages, direct and transitive: 0 carry known advisories, of which 0 are direct dependencies.

No known advisories affect the assessed dependencies.

An advisory means the version recorded in the dependency graph falls inside an advisory’s affected range. Reachability is not analysed, and the graph includes development and test pins — a finding may concern tooling rather than shipped software.

Raw JSON report machine-readable

Feedback

Spotted something off in this report, or have thoughts to share? Wrong measurements, missed tooling, ideas, questions — anything is welcome. Every message is read and gets a response.

The message is kept through sign-in.

Scores are signals, not warranties. They reflect publicly visible practices on GitHub — not a code audit, and not a security guarantee.

Missing data is excluded and weights renormalized, never scored as zero. Methodology is versioned and open: metrics v2.10.0, schema v0.31.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statisticsPyPI.