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name: model-api-components description: "Use and extend MMOCR registries, model components, data samples, transforms, metrics, visualizers, dictionaries, and OpenMMLab project extensions." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

MMOCR model API components

Use this sub-skill when the task is to inspect, build, register, customize, or troubleshoot MMOCR internals rather than to run an end-to-end OCR prediction or a full training job.

Best-fit tasks

• Register or build MMOCR model, transform, metric, visualizer, dictionary, or project-extension components through OpenMMLab registries.
• Decide which DataSample fields a custom head, postprocessor, metric, or visualizer should read and write.
• Add a custom backbone, head, postprocessor, metric, transform, or visualizer through a project module without editing the core MMOCR package.
• Diagnose registry/default-scope issues, dictionary token mismatches, transform pipeline type lookup failures, visualization/font/headless problems, and metric output-shape surprises.

Route these instead:

• End-to-end OCR/KIE inference, pretrained inferencers, or prediction result schemas: ../ocr-inference/.
• Train/test CLIs, config inheritance, work directories, checkpoints, and evaluator placement in full experiments: ../training-evaluation-configs/.
• Dataset conversion/preparation CLIs and dataset-zoo acquisition: ../data-preparation/.

Read this sub-skill

1. Start with component-api-reference for registry names, data structures, model families, dictionaries, transforms, metrics, visualizers, and utility entry points.
2. Use extension-patterns when adding custom modules through OpenMMLab project-style registration.
3. Use troubleshooting when a component cannot be built, registered, visualized, evaluated, or connected to the expected DataSample fields.
4. Use the bundled mmocr_component_registry_probe.py to inspect registry availability and dictionary files in the active Python environment.

Operating rules

• Prefer registry/config-based extension over direct edits to MMOCR core modules.
• Initialize or preserve the mmocr default scope before building components from config dicts.
• Import custom extension modules before building configs that name their registered classes.
• Treat TextDetDataSample, TextSpottingDataSample, TextRecogDataSample, and KIEDataSample as the contract between models, postprocessors, metrics, and visualizers.
• Keep dictionary files, token settings, postprocessor ignore characters, and recognition label fields synchronized.
• For component-level debugging, verify imports, registry membership, sample field presence, and small synthetic inputs before using full datasets or checkpoints.