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name: custom-models description: "Load EasyOCR custom recognition bundles, validate the file triad, and configure custom model and user-network directories." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

EasyOCR Custom Models

Use this sub-skill when the user has a custom recognition bundle and wants to load it with EasyOCR instead of the built-in recognition models.

Use when

• The task mentions recog_network='custom_example' or another custom stem.
• The user has .pth, .yaml, and .py files for a custom recognizer.
• The task is about user_network_directory, model_storage_directory, or EASYOCR_MODULE_PATH.
• The user wants to check whether a bundle is complete before running OCR.

Do not use when

• The user wants to train the model. That is out of scope here.
• The user wants DBNet or detector compilation. Use sub-skills/dbnet/.
• The task is ordinary OCR with built-in models. Use sub-skills/inference/.

Quick workflow

1. Read references/workflows.md for the bundle layout and load sequence.
2. Run scripts/check_bundle.py against the custom bundle directory or stem.
3. Confirm that the stem is the same across .pth, .yaml, and .py.
4. Load the bundle with easyocr.Reader(..., recog_network='<stem>').
5. Read references/troubleshooting.md if the bundle fails to load.

What this sub-skill owns

• The custom recognition bundle triad.
• Directory layout for user networks and model weights.
• The Reader constructor path that loads custom recognition code.
• Fast bundle validation before runtime.

References and scripts

• ../../references/api-reference.md for the main Reader signature.
• ../../references/configuration.md for cache directories and environment variable precedence.
• references/workflows.md for the bundle file layout and load pattern.
• references/troubleshooting.md for stem mismatches, YAML errors, and import failures.
• scripts/check_bundle.py for a safe bundle validator.

Boundary notes

• Keep training, dataset creation, and maintainer-only language regeneration out of this sub-skill.
• If the task needs a different detector backend, route to the DBNet sub-skill.
• If the task only needs built-in languages, route to inference instead.