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name: data-preparation description: "Prepare, inspect, and troubleshoot MMOCR dataset conversion and data layouts for text detection, recognition, spotting, and KIE." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

MMOCR Data Preparation

Use this sub-skill when the task is about OCR dataset preparation rather than model training, inference, or low-level data-sample internals. It covers MMOCR annotation schemas, dataset-zoo metadata, task-specific conversion choices, recognition LMDB handling, generated dataset configs, and safe preflight checks.

Start here

1. Read data formats and datasets to choose between textdet, textrecog, textspotting, and kie and to verify the annotation shape expected by MMOCR.

2. Run the bundled no-download preflight before any conversion or training:

   python scripts/mmocr_dataset_preflight.py --list
   python scripts/mmocr_dataset_preflight.py --dataset icdar2015 --task textdet
   

   Pass --dataset-zoo-path <dataset_zoo> when checking a project-local or private dataset-zoo directory.

3. Read data preparation workflows before planning official dataset preparation, private dataset-zoo entries, LMDB recognition data, generated config placement, or visualization.

4. Read troubleshooting when a dataset/task is unsupported, a converter wants network access, split selection is wrong, configs are not overwritten, images/annotations do not resolve, LMDB loading fails, text encodings are broken, KIE labels are inconsistent, or a dataset browser has no display.

Route here for

• Choosing --task textdet, textrecog, textspotting, or kie for a public or private OCR dataset.
• Inspecting dataset-zoo names, task coverage, license metadata, split support, and generated config names.
• Preparing MMOCR JSON annotations with data_list, img_path, bbox, polygon, text, ignore, and metainfo fields.
• Planning recognition crops, dictionaries, and optional LMDB output.
• Debugging data_root, data_prefix, ann_file, and pipeline loader interactions before training/evaluation.
• Treating dataset visualization as an optional, reference-only sanity check.

Route elsewhere

• Training/evaluation command construction, config inheritance, runner flags, metrics, checkpoints, and distributed launch: use training-evaluation-configs.
• Inferencer calls, prediction outputs, visualization outputs from inference, and OCR pipeline inference: use ocr-inference.
• DataSample class internals, registries, model components, transforms beyond loader compatibility, dictionaries as model components, and custom component extension: use model-api-components.

Source-script policy

• The bundled scripts/mmocr_dataset_preflight.py is the safe default. It only reads metadata or tiny annotations and never downloads datasets.
• The official unified dataset preparer can download, extract, move, delete, and write configs. Use its CLI semantics only after the user approves data, network, storage, and overwrite behavior.
• Dataset-specific converters and dataset browsing utilities are reference-only for this sub-skill: distill their behavior into a plan, keep sample counts tiny, and do not depend on GUI or large downloads for validation.