AI студия Владимира Ломтева
УСЛУГИПРОЕКТЫСТАТЬИБАЗА ЗНАНИЙМаркетплейсОбучениеПолезные сервисы

Оставьте заявку,
чтобы обсудить проект

Напишите ваш вопрос, не забудьте указать телефон. Мы перезвоним и все расскажем.

Контакты

Москва

Работаем по всей России
и миру (онлайн)

+7 (999) 760-24-41

Ежедневно с 9:00 до 21:00

lamooof@gmail.com

По вопросам сотрудничества

TelegramWhatsApp

Есть предложение?

Напишите нам в мессенджеры

© 2025 AI студия Владимира Ломтева

Политика конфиденциальностиСогласие на обработку ПДн|ИНН 623412173261
    Paddleocr Text Recognition — Скилл для ИИ-агентов | AI Рассвет

    Paddleocr Text Recognition

    Use this skill whenever the user wants text extracted from images, photos, scans, screenshots, or scanned PDFs. Returns exact machine readable strings with line level text and optional bbox coordinates. Strong accuracy for CJK, small print, and handwritten text. Trigger terms: OCR, 文字识别, 图片转文字, 截图识字, 提取图中文字, 扫描识字, 识字, 纯文字, plain text extraction, 坐标, 检测框, bbox, bounding box, image to text, screenshot, photo scan, recognize text.

    Скиллы для операций#GitHub#aidenwu0209/paddleocr-skills#skills.sh
    Скачивания
    0
    В избранном
    0
    Комментарии
    0
    Просмотры

    Установить скилл

    Добавьте инструмент одной командой или скачайте проверенный архив версии.

    npx skills add aidenwu0209/paddleocr-skills --skill paddleocr-text-recognition
    Скачать ZIP
    Версия
    1.0.0+e73477d877a7
    Автор
    Владимир Ломтев
    Репозиторий
    aidenwu0209/paddleocr-skills
    GitHub: aidenwu0209/paddleocr-skills
    1

    Установить скилл

    Добавьте инструмент одной командой или скачайте проверенный архив версии.

    npx skills add aidenwu0209/paddleocr-skills --skill paddleocr-text-recognition
    Скачать ZIP
    Версия
    1.0.0+e73477d877a7
    Автор
    Владимир Ломтев
    Репозиторий
    aidenwu0209/paddleocr-skills
    GitHub: aidenwu0209/paddleocr-skills

    Как установить

    1. 1Скопируйте команду из блока установки.
    2. 2Запустите её в терминале из каталога проекта.

    Документация

    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

    1. Identify the input source:

      • User provides URL: Use the --file-url parameter
      • User provides local file path: Use the --file-path parameter
    2. 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
    3. 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
    4. 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

    Alternative: paddleocr CLI

    This mirror keeps the bundled scripts/ocr_caller.py as the default path. The upstream PaddleOCR project (since PR #18090, 2026-06-03) also ships an official CLI that calls the same API directly. If the paddleocr package is installed, you can use it as a drop-in alternative — no uv run or local scripts required.

    Install (one-time):

    pip install "paddleocr>=3.7.0"
    

    Environment: the CLI only needs PADDLEOCR_ACCESS_TOKEN. It resolves the API endpoint internally, so PADDLEOCR_OCR_API_URL is not required when using the CLI (the URL is still required by the script).

    Basic OCR:

    ## From URL
    paddleocr api --model_type ocr --file_url "https://example.com/image.png"
    
    ## From local file
    paddleocr api --model_type ocr --file_path "./document.pdf"
    

    Common options:

    ## Specific model
    paddleocr api --model_type ocr --model PP-OCRv5 --file_path "./report.pdf"
    
    ## Disable preprocessing (faster, for flat/well-oriented images)
    paddleocr api --model_type ocr --file_path "./document.pdf" \
      --use_doc_unwarping False --use_doc_orientation_classify False
    
    ## Page ranges
    paddleocr api --model_type ocr --file_path "./large.pdf" --page_ranges "1-5,10,15-20"
    
    ## Save result to file
    paddleocr api --model_type ocr --file_url "https://..." --output result.json
    

    CLI output format — different from the script envelope:

    {
      "jobId": "job-xxx",
      "pages": [
        {
          "prunedResult": {
            "rec_texts": ["Line 1", "Line 2"],
            "rec_scores": [0.98, 0.95]
          },
          "ocrImageUrl": "https://..."
        }
      ]
    }
    

    The CLI prints {jobId, pages:[...]} to stdout. It does not wrap the response in the script's {ok, text, result, error} envelope, does not auto-save to a temp file, and does not concatenate text for you. If you switch paths, update your parsing logic accordingly.

    Scripts vs CLI — at a glance:

    Scripts (default) paddleocr CLI (alternative)
    Install uv resolves PEP 723 inline deps pip install "paddleocr>=3.7.0"
    Required env PADDLEOCR_OCR_API_URL + PADDLEOCR_ACCESS_TOKEN PADDLEOCR_ACCESS_TOKEN only
    Entry uv run scripts/ocr_caller.py ... paddleocr api --model_type ocr ...
    Output {ok, text, result, error} envelope, auto-saved to temp file {jobId, pages:[...]} to stdout
    Result location Path printed on stderr (or --output/--stdout) stdout (or --output)
    Best for Skills runtimes, offline-friendly, no extra install Already have paddleocr installed, want the upstream-canonical flow

    Run paddleocr api --help for the full option list.

    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:

    1. Show the exact error message to the user.

    2. 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.

    3. 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.

    Требования и возможности

    Источник пакета
    https://github.com/aidenwu0209/paddleocr-skills/tree/e73477d877a726b8233d02d0b3a8638f95cd6e96/skills/paddleocr-text-recognition

    Файлы версии

    ПутьРазмерSHA256
    SKILL.md125041143eee7d2eb7e71...
    references/output_schema.md30942e9a5a0ec2fd1680...
    scripts/lib.py122576771afed3ca2acc3...
    scripts/ocr_caller.py4475d9899aef178a5df5...
    scripts/smoke_test.py41470917b3f0ce067a2f...

    Частые вопросы

    Как установить Paddleocr Text Recognition?
    Используйте команду npx skills add aidenwu0209/paddleocr-skills --skill paddleocr-text-recognition или скачайте ZIP-архив.
    Можно ли скачать Paddleocr Text Recognition бесплатно?
    Да, опубликованную версию можно скачать из маркетплейса бесплатно.

    Похожие инструменты

    Смотреть все
    Ozon Seller и Performance APIСправочник Ozon Seller API и Performance API с локальными OpenAPI-спецификациями.Grill MeA relentless interview to sharpen a plan or design.Excel Automation>Text To LottieCreate, edit, or fix Lottie/Bodymovin JSON animations for the local Skia Skottie player. Use for text-to-Lottie, SVG/logo/type animation, loaders/icons, state feedback, UI microinteractions, lower thirds, diagrams, data/stat/chart animations, product promos, scene/camera motion, visual effects, scene edits, slots/controls, and Skottie debugging.
    Комментарии

    Войдите, чтобы оставить комментарий.

    Комментариев пока нет.