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생산성 및 협업 · SKILL.md

inference

inference

Run EasyOCR on images and image-like inputs with the Reader API or CLI, including batching, box extraction, and output formatting.

Independent GitHub snapshot · Automated classification · Original documentation governs

Skill 문서

GitHub

name: inference description: "Run EasyOCR on images and image-like inputs with the Reader API or CLI, including batching, box extraction, and output formatting." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

EasyOCR Inference

Use this sub-skill for ordinary OCR work: read text from an image, screenshot, scan, crop, or batch of same-sized images with EasyOCR's built-in models.

Use when

  • The user wants text extracted from an image or screenshot.
  • The task mentions readtext, detect, recognize, or readtext_batched.
  • The task mentions the EasyOCR CLI.
  • The user wants boxes, paragraphs, detail=0, JSON/dict output, or allowlist/blocklist filtering.

Do not use when

  • The user is loading a custom recognition bundle. Use sub-skills/custom-models/.
  • The user is enabling DBNet or compiling DCN. Use sub-skills/dbnet/.
  • The task is training or ONNX export. Those are out of scope for this skill.

Quick workflow

  1. Confirm the language codes and backend choice with ../../references/configuration.md.
  2. Run ../../scripts/inspect_runtime.py if you want a fast install/backends smoke before a real OCR call.
  3. Use scripts/readtext_smoke.py for a single-image OCR sanity check.
  4. Read references/workflows.md for end-to-end patterns.
  5. Read references/troubleshooting.md when the result or CLI behavior looks wrong.

Typical inputs and outputs

  • Inputs: file path, NumPy array, raw bytes, or a raw image URL.
  • Outputs: strings, (box, text, confidence) tuples, dicts, or JSON strings, depending on detail and output_format.
  • readtext_batched returns one OCR result list per input image.
  • detect returns boxes first; recognize consumes boxes or a full crop.

Common control points

  • gpu: automatic accelerator selection or explicit CPU/device choice.
  • download_enabled: whether missing model files may be downloaded.
  • paragraph, rotation_info, allowlist, blocklist, detail, and output_format.
  • batch_size and workers for throughput tuning.

References and scripts

  • ../../references/api-reference.md for public signatures and return shapes.
  • ../../references/cli-reference.md for the CLI flag groups and parser caveats.
  • ../../references/configuration.md for cache paths, backend behavior, and language/model rules.
  • references/workflows.md for practical OCR examples.
  • references/troubleshooting.md for CLI quirks, model-cache issues, and legacy helper caveats.
  • scripts/readtext_smoke.py for a tiny OCR run against one image.

Boundary notes

  • Keep custom bundle loading, detector compilation, and training guidance in their owning sub-skills.
  • Prefer the Python API for exact control when the CLI's current parser quirks make a flag hard to use reliably.
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