Public record
Software health reportschema 0.13.0 · metrics 2.10.0 · 2026-07-18 16:10 UTC

kha-white / manga_ocr

Optical character recognition for Japanese text, with the main focus being Japanese manga

PythonApache-2.0★ 2,726 stars⑂ 135 forkssince Jan 2022View on GitHub ↗
KindCommand-line toolhow this is determined

kha-white/manga_ocr holds a health index of 50 out of 100, placing it in the Moderate band. It scores highest on Community & Adoption (67/100) and lowest on Security (32/100). It was last updated 6 days ago. A single contributor accounts for most of its recent work.

50
overall / 100
Moderate

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.

50
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 50 is calibrated to 50 on the published index scale (record calibration 2026-08-02).

Ownership

Maciej BudyśPersonal account
57 followers6 public repossince Oct 2016

This repository is owned by a personal account. A single-owner project carries more continuity risk than an organization-backed one.

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPImanga-ocrpoints to another repo — not scored0.1.1525,543176 days ago

Metrics by category

Vitality

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

59Moderate · 21% of overall
How it's scored
36/36Push recencylast push 6 days ago
0.7/36Commit cadence1/52 weeks with commits
4.3/18Commit volume2 commits in the last year
1/10OpenSSF Scorecard: Maintained2 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 1
Inputs used
commits_last_year2
human_commit_share
days_since_last_push6
active_weeks_last_year1
How it's scored
27/27Ships releases5 releases published
36/36Release recencylatest release 6 days ago
12.6/27Release cadencea release every ~187.5 days
8/10OpenSSF Scorecard: Signed-Releases5 out of the last 5 releases have a total of 5 signed artifacts.
Inputs used
releases_count5
latest_release_tagv0.1.15
releases_from_tagsno
days_since_latest_release6
mean_days_between_releases187.5

Community & Adoption

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

67Good · 17% of overall
How it's scored
55.7/60Stars2,726 stars
17.7/25Forks135 forks
7.2/15Watchers21 watchers
Inputs used
forks135
stars2,726
watchers21
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_badges
has_contributingno
has_issue_templateno
has_code_of_conductno
readme_badge_services
has_pull_request_templateno

Sustainability & Governance

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

43Weak · 23% of overall
How it's scored
9/54Bus factor1 contributor(s) cover half of all commits
3.1/22.5Commit distributiontop contributor authored 86% of commits
12.2/13.5Contributor breadth9 contributors
0/10OpenSSF Scorecard: Contributorsproject has 0 contributing companies or organizations -- score normalized to 0
Inputs used
bus_factor1
contributors_sampled9
top_contributor_share0.862
How it's scored
22.1/42Issue resolution52% of issues closed
25.3/30PR acceptance16/19 decided PRs merged
0/13Newcomer PR acceptanceno first-time contributor's PR decided in 30d
3/15OpenSSF Scorecard: Code-ReviewFound 7/29 approved changesets -- score normalized to 2
Inputs used
merged_prs16
open_issues38
closed_issues42
prs_merged_7d
prs_decided_7d
prs_merged_30d
prs_decided_30d
issue_closed_ratio0.525
closed_unmerged_prs3
first_time_authors_30d
first_time_prs_merged_30d
first_time_prs_decided_30d
Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.
How it's scored
10/30Ownership backingpersonal (user) account
0/20Verified domainnot applicable to user accounts
12.7/25Owner reach57 followers of kha-white
18.2/25Track record6 public repos, account ~9 yr old
Inputs used
followers57
owner_typeUser
is_verified
owner_loginkha-white
public_repos6
account_age_days3,569
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Engineering Quality

Are baseline engineering and documentation practices in place?

53Moderate · 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-Tests1 out of 15 merged PRs checked by a CI test -- score normalized to 0
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configno
has_precommit_configno

Documentation

60Moderate
How it's scored
30/30README
0/25Documentation directory
0/15Documentation / homepage site
10/10Repository description
10/10Topics7 topics
10/10Wiki
Inputs used
topicsocr, japanese, manga, transformers, computer-vision, deep-learning, comics
has_wikiyes
homepage
docs_site
has_readmeyes
has_docs_dirno
has_descriptionyes

Security

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

32At Risk · 16% of overall
How it's scored
7.5/7.5Binary-Artifactsno binaries found in the repo
0/7.5Branch-Protectionbranch protection not enabled on development/release branches
0/2.5CI-Tests1 out of 15 merged PRs checked by a CI test -- score normalized to 0
0/2.5CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
1.5/7.5Code-ReviewFound 7/29 approved changesets -- score normalized to 2
0/2.5Contributorsproject has 0 contributing companies or organizations -- score normalized to 0
10/10Dangerous-Workflowno dangerous workflow patterns detected
0/7.5Dependency-Update-Toolno update tool detected
0/5Fuzzingproject is not fuzzed
2.5/2.5Licenselicense file detected
0.8/7.5Maintained2 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 1
5/5Packagingpackaging workflow detected
0/5Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0/5SASTSAST tool is not run on all commits -- score normalized to 0
0/5Security-Policysecurity policy file not detected
6/7.5Signed-Releases5 out of the last 5 releases have a total of 5 signed artifacts.
0/7.5Token-Permissionsdetected GitHub workflow tokens with excessive permissions
0/7.5Vulnerabilities29 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated18
scorecard_versionv5.5.0
checks_inconclusive0
scorecard_aggregate3.2

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.

34At Risk · 4% of overall
How it's scored
0/45Agent instructionsno CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
0/40Legible commit historyno data
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share
agent_instruction_files
agent_instruction_max_bytes
Excluded from scoring (no data or not applicable): Legible commit history. Remaining weights renormalized.
How it's scored
0/18One-command bootstrap
22/22Automated tests
0/11Lint / format config
0/11Static type checking
0/10Reproducible environment
0/10Demonstrated agent practiceno data
0/8Automated maintenanceno data
0/10OpenSSF Scorecard: Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfiles
has_dockerfileno
typed_languageno
bootstrap_files
has_devcontainerno
has_linter_configno
typecheck_configs
agent_commit_share
toolchain_manifests
dependency_bot_commit_share
Excluded from scoring (no data or not applicable): Demonstrated agent practice, Automated maintenance. Remaining weights renormalized.
How it's scored
0/45Type-checkable codePython without a type-check config
55/55Manageable file sizes0/25 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes10,269
source_files_sampled25
oversized_source_files0
How it's scored
0/40API schema (OpenAPI/GraphQL/proto)not applicable to this kind of software
0/20MCP servernot applicable to this kind of software
40/40Runnable examplesexamples
Inputs used
example_dirsexamples
has_mcp_signalno
api_schema_files
interfaces_expected_of
Excluded from scoring (no data or not applicable): API schema (OpenAPI/GraphQL/proto), MCP server. Remaining weights renormalized.

Key facts

2,726GitHub stars
9contributors
2commits, last 12 months
6days since last push
5releases
1bus factor
38open issues
PyPIpackage ecosystems

Data collection warnings

  • pypi package 'manga-ocr' points at a different repository (https://github.com/kha-white/manga-ocr); excluded from ecosystem scoring

More detail

OpenSSF Scorecard 3.2 / 10
3.2aggregate

Independent, tool-agnostic security assessment from the open-source OpenSSF Scorecard. Each check rewards a security practice, not a specific vendor's tool. Checks Scorecard could not determine are marked n/a and excluded from the security score (never counted as zero).Scorecard v5.5.0 · 2026-07-18 16:09 UTC

10Binary-Artifactsno binaries found in the repo
0Branch-Protectionbranch protection not enabled on development/release branches
0CI-Tests1 out of 15 merged PRs checked by a CI test -- score normalized to 0
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
2Code-ReviewFound 7/29 approved changesets -- score normalized to 2
0Contributorsproject has 0 contributing companies or organizations -- score normalized to 0
10Dangerous-Workflowno dangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
1Maintained2 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 1
10Packagingpackaging workflow detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0SASTSAST tool is not run on all commits -- score normalized to 0
0Security-Policysecurity policy file not detected
8Signed-Releases5 out of the last 5 releases have a total of 5 signed artifacts.
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
0Vulnerabilities29 existing vulnerabilities detected
Direct dependencies 10
RegistryPackageVersion constraintManifest
PyPIfirepyproject.toml
PyPIfugashipyproject.toml
PyPIjaconvpyproject.toml
PyPIlogurupyproject.toml
PyPInumpypyproject.toml
PyPIPillow>=10.0.0pyproject.toml
PyPIpyperclippyproject.toml
PyPItorch>=1.0pyproject.toml
PyPItransformers>=4.25.0pyproject.toml
PyPIunidic_litepyproject.toml
All dependencies 29

Full resolved dependency set from the GitHub dependency graph: 10 direct and 19 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIfiredirect
PyPIfugashidirect
PyPIjaconvdirect
PyPIlogurudirect
PyPInumpydirect
PyPIpillowdirect
PyPIpyperclipdirect
PyPItorchdirect
PyPItransformersdirect
PyPIunidic-litedirect
PyPIalbumentationsindirect
PyPIbudouindirect
PyPIdatasetsindirect
PyPIhtml2imageindirect
PyPIipadicindirect
PyPIjiwerindirect
PyPImatplotlibindirect
PyPImecab-python3indirect
PyPIopencv-pythonindirect
PyPIpandasindirect
PyPIpytestindirect
PyPIscikit-imageindirect
PyPIscikit-learnindirect
PyPIscipyindirect
PyPIsetuptoolsindirect
PyPItorchinfoindirect
PyPItorchvisionindirect
PyPItqdmindirect
PyPIwandbindirect
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 v0.13.0 — full methodology · metrics wiki.

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