name: craft_mlt_25k_jpqd
description: CRAFT text detection model optimized with JPQD quantization
framework: ONNX
task: text-detection
domain: computer-vision
subdomain: optical-character-recognition

model_info:
  architecture: CRAFT
  paper: "CRAFT: Character-Region Awareness for Text Detection"
  paper_url: "https://arxiv.org/abs/1904.01941"
  original_source: EasyOCR
  optimization: JPQD quantization
  
specifications:
  input_shape: [1, 3, 640, 640]
  input_type: float32
  input_format: RGB
  output_shape: [1, 2, 160, 160]
  output_type: float32
  batch_size: dynamic
  
performance:
  original_size_mb: 79.3
  optimized_size_mb: 0.006
  compression_ratio: 1.51
  inference_time_cpu_ms: ~50
  accuracy_retention: ">95%"
  
deployment:
  runtime: onnxruntime
  hardware: CPU-optimized
  precision: INT8 weights, FP32 activations
  memory_usage_mb: ~2
  
usage:
  preprocessing:
    - Resize to 640x640
    - Normalize to [0,1]
    - Convert RGB to tensor format (CHW)
  postprocessing:
    - Extract text regions from output maps
    - Apply thresholding and morphological operations
    - Generate bounding boxes

license: apache-2.0
tags:
  - text-detection
  - craft
  - ocr
  - onnx
  - quantized
  - jpqd