paddleocr-text-recognition

SKILL.md

PaddleOCR Text Recognition Skill

When to Use This Skill

Trigger keywords (routing): Bilingual trigger terms (Chinese and English) are listed in the YAML description above—use that field for discovery and routing.

Use this skill for:

Extract text from images (screenshots, photos, scans)

Extract text from PDFs or document images when the goal is line/box-level text, not recovering table grids, formulas, or full reading-order layout

Extract text from URLs or local files that point to images/PDFs

Do not use for:

Plain text files, code files, or markdown documents that can be read directly as text

Documents with tables, formulas, charts, or complex layouts — use Document Parsing instead

Tasks that do not involve image-to-text conversion

Installation
Scripts declare their dependencies inline (PEP 723). No separate install step is needed — uv resolves dependencies automatically:

uv run scripts/ocr_caller.py --help

How to Use This Skill

Working directory: All uv run scripts/... commands below should be run from this skill's root directory (the directory containing this SKILL.md file).

Basic Workflow

Identify the input source:

User provides URL: Use the --file-url parameter

User provides local file path: Use the --file-path parameter

Execute OCR:

uv run scripts/ocr_caller.py --file-url "URL provided by user" --pretty

Or for local files:

uv run scripts/ocr_caller.py --file-path "file path" --pretty

Performance note: Parsing time scales with document complexity. Single-page images typically complete in 1-3 seconds; large PDFs (50+ pages) may take several minutes. Allow adequate time before assuming a timeout.

Default behavior: save raw JSON to a temp file:

If --output is omitted, the script saves automatically under the system temp directory

Default path pattern: <system-temp>/paddleocr/text-recognition/results/result_<timestamp>_<id>.json

If --output is provided, it overrides the default temp-file destination

If --stdout is provided, JSON is printed to stdout and no file is saved

In save mode, the script prints the absolute saved path on stderr: Result saved to: /absolute/path/...

In default/custom save mode, read and parse the saved JSON file before responding

Use --stdout only when you explicitly want to skip file persistence

Parse JSON response:

In default/custom save mode, load JSON from the saved file path shown by the script

Check the ok field: true means success, false means error

Extract text: text field contains all recognized text

If --stdout is used, parse the stdout JSON directly

Handle errors: If ok is false, display error.message

Present results to user:

Display extracted text in a readable format

If the text is empty, the image may contain no text

In save mode, always tell the user the saved file path and that full raw JSON is available there

What to Do After Extraction

Common next steps once you have the recognized text:

Save to file: Write the text field to a .txt or .md file

Search the content: Search the saved output file for keywords

Feed to another pipeline: The text field is clean plain text, ready for downstream processing

Poor results: See "Tips for Better Results" below before retrying

Complete Output Display

Always display the COMPLETE recognized text to the user. The user typically needs the full content for downstream use — truncation silently loses data they may not notice is missing.

Display the entire text field, no matter how long

Do not use phrases like "Here's a summary" or "The text begins with..."

Do not truncate with "..." unless the text truly exceeds reasonable display limits (>10,000 chars)

Example - Correct:

User: "Extract the text from this image"
Agent: I've extracted the text from the image. Here's the complete content:

[Display the entire text here]

Example - Incorrect:

User: "Extract the text from this image"
Agent: I found some text in the image. Here's a preview:
"The quick brown fox..." (truncated)

Understanding the Output

The script returns a JSON envelope with ok, text, result, and error fields. Use text for the recognized content; result contains the raw API response for debugging.

For the full schema and field-level details, see references/output_schema.md.

Raw result location (default): the temp-file path printed by the script on stderr

Usage Examples

Example 1: URL OCR

uv run scripts/ocr_caller.py --file-url "https://example.com/invoice.jpg" --pretty

Example 2: Local File OCR

uv run scripts/ocr_caller.py --file-path "./document.pdf" --pretty

Example 3: OCR With Explicit File Type

uv run scripts/ocr_caller.py --file-url "https://example.com/input" --file-type 1 --pretty

--file-type 0: PDF

--file-type 1: image

If omitted, the type is auto-detected from the file extension. For local files, a recognized extension (.pdf, .png, .jpg, .jpeg, .bmp, .tiff, .tif, .webp) is required; otherwise pass --file-type explicitly. For URLs with unrecognized extensions, the service attempts inference.

Example 4: Print JSON Without Saving

uv run scripts/ocr_caller.py --file-url "https://example.com/input" --stdout --pretty

First-Time Configuration

When API is not configured, the script outputs:

{
  "ok": false,
  "text": "",
  "result": null,
  "error": {
    "code": "CONFIG_ERROR",
    "message": "PADDLEOCR_OCR_API_URL not configured. Get your API at: https://paddleocr.com"
  }
}

Configuration workflow:

Show the exact error message to the user.

Guide the user to obtain credentials: Visit the PaddleOCR website, click API, select the PP-OCRv5 model, select the language, then copy the API_URL and Token. They map to these environment variables:

PADDLEOCR_OCR_API_URL — full endpoint URL ending with /ocr

PADDLEOCR_ACCESS_TOKEN — 40-character alphanumeric string

Optionally configure PADDLEOCR_OCR_TIMEOUT for request timeout. Recommend using the host application's standard configuration method rather than pasting credentials in chat.

Apply credentials — one of:

User configured via the host UI: ask the user to confirm, then retry.

User pastes credentials in chat: warn that they may be stored in conversation history, help the user persist them using the host's standard configuration method, then retry.

Error Handling

All errors return JSON with ok: false. Show the error message and stop — do not fall back to your own vision capabilities. Identify the issue from error.code and error.message:

Authentication failed (403) — error.message contains "Authentication failed"

Token is invalid, reconfigure with correct credentials

Quota exceeded (429) — error.message contains "API rate limit exceeded"

Daily API quota exhausted, inform user to wait or upgrade

Unsupported format — error.message contains "Unsupported file format"

File format not supported, convert to PDF/PNG/JPG

No text detected:

text field is empty

Image may be blank, corrupted, or contain no text

Tips for Better Results

If recognition quality is poor:

Low resolution: Provide a higher resolution image (≥300 DPI works well for most printed text)

Noisy background: A cleaner scan or screenshot typically yields better results than a phone photo

Check confidence: The raw JSON (result.result.ocrResults[n].prunedResult.rec_scores) shows per-line confidence scores — low values identify uncertain regions worth reviewing

Reference Documentation

references/output_schema.md — Full output schema, field descriptions, and command examples

Note: Model version, capabilities, and supported file formats are determined by your API endpoint (PADDLEOCR_OCR_API_URL) and its official API documentation.

Testing the Skill

To verify the skill is working properly:

uv run scripts/smoke_test.py
uv run scripts/smoke_test.py --skip-api-test
uv run scripts/smoke_test.py --test-url "https://..."

The first form tests configuration and API connectivity. --skip-api-test checks configuration only. --test-url overrides the default sample image URL.