---

name: mmocr description: "Use MMOCR for OCR inference, dataset preparation, config-driven training/evaluation, model components, registries, and OpenMMLab OCR troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

MMOCR

Use this repo skill when a task involves OpenMMLab MMOCR: text detection, text recognition, OCR chains, text spotting, key information extraction (KIE), OCR datasets, config-driven training/evaluation, or MMOCR component extension.

Start here

1. Read references/package-overview.md for the package capability map, public install shape, backend expectations, and bundled helper summary.
2. Run scripts/check_mmocr_environment.py when you need to verify imports, dependency versions, CUDA visibility, or optional config loading.
3. Use the route map below for workflow details.
4. Read references/troubleshooting.md for cross-cutting install/import/backend/download/headless-display failures.
5. Check references/repo-provenance.md before deciding whether this skill matches a newer checkout or package version.

Minimal public install and import check

MMOCR depends on PyTorch, MMEngine, MMCV, and MMDetection. The project recommends OpenMIM for OpenMMLab packages:

pip install -U openmim
mim install mmengine
mim install mmcv
mim install mmdet
mim install mmocr

Then verify the environment from any working directory:

python scripts/check_mmocr_environment.py

For config work, verify that the config loads and uses the MMOCR registry scope:

python scripts/check_mmocr_environment.py --config CONFIG --require-default-scope mmocr

A CPU import/config check is not proof of CUDA, distributed, or Slurm readiness. Require real backend checks before claiming those paths work.

Route map

User asks for...	Read
OCR inference over images/folders/arrays, MMOCRInferencer, task inferencers, saved predictions/visualizations, model aliases, KIE chains, or headless inference troubleshooting	sub-skills/ocr-inference/SKILL.md
Config inspection, model-family selection, training/testing/evaluation command construction, work directories, checkpoints, AMP, TTA, distributed launch, or Slurm routing	sub-skills/training-evaluation-configs/SKILL.md
Dataset preparation, dataset_zoo-style metadata, textdet/textrecog/textspotting/KIE annotation formats, LMDB recognition data, or dataset-layout debugging	sub-skills/data-preparation/SKILL.md
Registries, DataSamples, transforms, model components, dictionaries, metrics, visualizers, custom modules, or OpenMMLab project extensions	sub-skills/model-api-components/SKILL.md

Common decisions

• For image prediction, use ocr-inference; for dataset evaluation against a checkpoint, use training-evaluation-configs.
• For any train/test command, run the config smoke script first and confirm data/checkpoint availability before launching expensive work.
• For private datasets, start with data-preparation and validate the annotation/task choice before editing training configs.
• For registry or custom module errors, use model-api-components before trying another long run.
• For remote servers, prefer saved visualization files over GUI display.
• For pretrained aliases or dataset preparers, ask before using network downloads or shared caches.

Backend policy

• CPU is enough for import checks, config smoke, dataset metadata preflight, registry probes, and many troubleshooting tasks.
• CUDA/GPU is optional but required for honest accelerated inference/training/evaluation verification.
• Distributed/NCCL and Slurm are optional operational backends; do not infer them from CPU success.
• Long training, large evaluation, model downloads, dataset downloads, and scheduler jobs need explicit user approval.

Bundled helpers

• scripts/check_mmocr_environment.py: root import/version/backend/config smoke.
• sub-skills/ocr-inference/scripts/mmocr_inference_smoke.py: inference preflight and opt-in execution helper.
• sub-skills/training-evaluation-configs/scripts/mmocr_config_smoke.py: config summary without training.
• sub-skills/data-preparation/scripts/mmocr_dataset_preflight.py: dataset metadata and tiny annotation checks.
• sub-skills/model-api-components/scripts/mmocr_component_registry_probe.py: registry, DataSample, and dictionary probe.

Non-goals

• This skill does not replace MMOCR's package installation or model weights.
• It does not bundle full training/evaluation launchers, pretrained checkpoints, datasets, or GUI assets.
• It does not claim CUDA, distributed, or Slurm readiness unless the caller verifies those backends in their runtime.