---

name: ocr-inference description: "Use MMOCR inference APIs and safe CLI preflights for text detection, recognition, OCR chains, KIE, text spotting, outputs, and visualization." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

ocr-inference

Use this sub-skill when the task is to run or debug MMOCR inference: text detection, text recognition, a detection+recognition OCR chain, KIE on OCR results, text-spotting inference notes, visualization, prediction dumping, model/checkpoint/device selection, and safe preflight checks.

Route first

• For concrete OCR/KIE inference calls, start with Inference workflows.
• For exact class signatures, accepted inputs, output keys, batching, devices, model names, and save/visualization arguments, use API reference.
• For failure triage, especially KIE initialization, model-name errors, missing checkpoints, headless visualization, or output-directory confusion, use Troubleshooting.
• For a no-download preflight CLI, use the bundled MMOCR inference smoke helper.

Boundaries

Use sibling sub-skills instead of this one when the task is outside runtime inference:

• Training, testing, metric evaluation, schedule changes, config editing, or checkpoint evaluation: ../training-evaluation-configs/.
• Dataset preparation, annotation conversion, dataset-zoo mechanics, dictionaries, or data-format migration: ../data-preparation/.
• Model internals, custom backbones/heads/losses/postprocessors/registries, or project component implementation: ../model-api-components/.

Safe default operating pattern

1. Decide the mode: detection only, recognition only, OCR chain, KIE chain, direct KIEInferencer, or TextSpotInferencer.
2. Prefer local config+checkpoint paths for offline or reproducible work. Passing a model-zoo name without a local weight may trigger a model download/cache lookup during real inference.
3. Use device="cpu" when CUDA availability is unknown. Use device="cuda:0" only after confirming CUDA is available.
4. In headless sessions, keep show=False; use out_dir, save_pred=True, and save_vis=True to produce JSON and visualization files.
5. For KIE through MMOCRInferencer, initialize with all three components: det, rec, and kie. KIE without both detection and recognition is invalid.
6. If a save flag is enabled, keep out_dir non-empty. MMOCR writes predictions below preds/ and visualizations below vis/ inside that output directory.

Minimal examples

Headless OCR chain with the high-level inferencer:

from mmocr.apis import MMOCRInferencer

ocr = MMOCRInferencer(det="DBNet", rec="CRNN", device="cpu")
result = ocr(
    "image_or_folder",
    batch_size=1,
    det_batch_size=1,
    rec_batch_size=4,
    out_dir="mmocr_outputs/ocr",
    save_pred=True,
    save_vis=True,
    print_result=True,
    show=False,
)
print(result["predictions"][0].keys())

No-download preflight from this sub-skill directory:

python scripts/mmocr_inference_smoke.py image_or_folder --det DBNet --rec CRNN --device cpu

The helper only preflights by default. Add --execute only when local weights are supplied or --allow-download is intentionally set.