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craft-mlt-25k
inference4j/craft-mlt-25k
craft-mlt-25k is a machine learning model from inference4j. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for onnx. The card lists the license as mit.
ONNX export of CRAFT (Character Region Awareness for Text Detection) trained on SynthText + IC13/IC17 (MLT 25K variant).
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Updated Feb 14, 2026
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At a glance
- Library
- onnx
- License
- mit
- Access
- Public
- Created
- Feb 13, 2026
- Updated
- Feb 14, 2026
- SHA
- 290e4d5e
Model card
From the Hugging Face model README
CRAFT Text Detection (MLT 25K) — ONNX
ONNX export of CRAFT (Character Region Awareness for Text Detection) trained on SynthText + IC13/IC17 (MLT 25K variant).
Converted for use with inference4j, an inference-only AI library for Java.
Usage with inference4j
try (CraftTextDetector detector = CraftTextDetector.builder().build()) {
List<TextRegion> regions = detector.detect(Path.of("document.jpg"));
for (TextRegion r : regions) {
System.out.printf("Text at [%.0f, %.0f, %.0f, %.0f] (confidence=%.2f)%n",
r.box().x1(), r.box().y1(), r.box().x2(), r.box().y2(),
r.confidence());
}
}
Model Details
| Property | Value |
|---|---|
| Architecture | VGG16-BN backbone + U-Net decoder |
| Task | Text detection (character-level region + affinity maps) |
| Training data | SynthText + ICDAR 2013/2017 (MLT) |
| Weights | craft_mlt_25k.pth from clovaai/CRAFT-pytorch |
| ONNX opset | 17 |
| Input | [batch, 3, height, width] — RGB, ImageNet-normalized, dimensions must be multiples of 32 |
| Output: score_map | [batch, height/2, width/2, 2] — channel 0: region score, channel 1: affinity score |
| Output: feature_map | [batch, 32, height/2, width/2] — intermediate features (optional, for refinement) |
| Dynamic axes | Batch, height, and width are dynamic |
Preprocessing
- Resize maintaining aspect ratio (long side to target size, e.g. 1280)
- Round both dimensions to nearest multiple of 32
- ImageNet normalization:
(pixel / 255 - mean) / std- mean =
[0.485, 0.456, 0.406] - std =
[0.229, 0.224, 0.225]
- mean =
- NCHW layout:
[1, 3, H, W]
Postprocessing
- Combine:
clip(region_score + affinity_score, 0, 1) - Binary threshold at
low_text_threshold(default 0.4) - Connected component labeling (4-connectivity)
- For each component: compute mean region score, filter by
text_threshold(default 0.7) - Extract axis-aligned bounding box, scale back to original image coordinates
Original Paper
Baek, Y., Lee, B., Han, D., Yun, S., & Lee, H. (2019). Character Region Awareness for Text Detection. CVPR 2019. arXiv:1904.01941
License
The original CRAFT model weights are released under the MIT License by Clova AI Research (NAVER Corp).
- Library
- onnx
- License
- mit
- Created
- Feb 13, 2026
- Updated
- Feb 14, 2026