ddddocr

name: ddddocr description: "DDDDOCR OCR recognition service with MCP protocol support. Provides optical character recognition, object detection, and slide matching capabilities. Use for: Recognizing text from captcha images, Detecting objects/text regions in images, Matching slide positions for verification codes, Performing any OCR-related tasks through MCP protocol."

DDDDOCR Service

Quick Start

Start the ddddocr service with all features enabled:

python scripts/start_ddddocr.py

The script automatically:

• Checks if service is already running
• Downloads the latest ddddocr binary for current platform if needed
• Starts service with ocr, det, slide, and mcp features
• Binds to 127.0.0.1:8000 by default

Command Line Tools

Use the provided scripts for quick OCR operations:

OCR Recognition

python scripts/ocr.py <image_path> [--color-filter FILTER] [--charset-range RANGE] [--text-only]

Examples:

python scripts/ocr.py image/3.png
python scripts/ocr.py image/3.png --text-only
python scripts/ocr.py image/3.png --color-filter green --charset-range "0123456789"

Object Detection

python scripts/det.py <image_path> [--json]

Examples:

python scripts/det.py image/3.png
python scripts/det.py image/3.png --json

Slide Matching

python scripts/slide.py <target_path> <background_path> [--algorithm match|comparison] [--simple-target] [--json]

Examples:

python scripts/slide.py image/su.png image/bg.png
python scripts/slide.py image/su.png image/bg.png --algorithm comparison
python scripts/slide.py image/target.png image/bg.png --simple-target --json

Core Capabilities

1. OCR Recognition

Recognize text from images, supports color filtering and character range specification.

Use cases:

• Captcha recognition (numeric, alphanumeric, Chinese)
• Text extraction from images
• Custom character set recognition

Endpoint: POST /ocr

2. Object Detection

Detect text regions and objects in images.

Use cases:

• Point-and-click captcha verification
• Text region localization
• Multiple object detection

Endpoint: POST /det

3. Slide Matching

Match slide images with background positions.

Algorithm 1 (slide-match): Template matching for transparent slides Algorithm 2 (slide-comparison): Difference-based comparison

Use cases:

• Slide captcha verification
• Image positioning

Endpoints: POST /slide-match, POST /slide-comparison

MCP Protocol

The service implements the Model Context Protocol for AI agent integration.

Endpoint: POST http://127.0.0.1:8000/mcp

Available MCP tools:

• ocr - OCR recognition with optional color filtering and character range
• det - Object detection returning bounding boxes
• slide_match - Slide matching (algorithm 1)
• slide_comparison - Slide comparison (algorithm 2)

See references/mcp.md for MCP protocol details.

REST API

The service also provides a REST API:

See references/api.md for detailed API documentation.

Usage Examples

OCR Recognition

import requests
import base64

with open("image.png", "rb") as f:
    image_b64 = base64.b64encode(f.read()).decode()

response = requests.post("http://127.0.0.1:8000/ocr", json={
    "image": image_b64,
    "color_filter": "green",
    "charset_range": "0123456789"
})

print(response.json())

Object Detection

response = requests.post("http://127.0.0.1:8000/det", json={
    "image": image_b64
})

print(response.json())

Slide Matching

with open("target.png", "rb") as f:
    target_b64 = base64.b64encode(f.read()).decode()
with open("background.png", "rb") as f:
    bg_b64 = base64.b64encode(f.read()).decode()

response = requests.post("http://127.0.0.1:8000/slide-match", json={
    "target_image": target_b64,
    "background_image": bg_b64,
    "simple_target": True
})

print(response.json())

Color Filtering

Supported presets: red, blue, green, yellow, orange, purple, cyan, black, white, gray

HSV ranges can also be specified as array of tuples: [(min_h, min_s, min_v), (max_h, max_s, max_v)]

Character Range Values

Custom string can also be used: "0123456789+-x/=?"

Service Status

Check if service is running:

curl http://127.0.0.1:8000/status

Response:

{
  "code": 200,
  "msg": "success",
  "data": {
    "service_status": "running",
    "enabled_features": ["ocr", "det", "slide", "mcp"]
  }
}