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README.md

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+---
+license: mit
+language:
+  - en
+  - zh
+tags:
+  - ocr
+  - captcha
+  - crnn
+  - ctc
+  - onnx
+library_name: onnx
+pipeline_tag: image-to-text
+---
+
+# cpdaily-ocr
+
+A lightweight CRNN+CTC OCR model for recognizing 5-character alphanumeric captchas
+(随机彩色斜体、旋转, 白底无干扰线 / random colored italic & rotated glyphs on a clean
+white background). Trained from scratch on real captcha samples, exported to pure ONNX
+for dependency-free inference (works with `tract`, `onnxruntime`, etc.).
+
+一个轻量级 CRNN+CTC 验证码识别模型, 识别 5 位字母数字验证码。从真实样本自训, 导出为
+纯 ONNX, 推理无任何 Python 依赖。
+
+## Files / 文件
+
+| File | Size | Description |
+|---|---|---|
+| `cpdaily_captcha_ocr.onnx` | 2.24 MB | fp32 full-precision master / fp32 全精度母本 |
+| `cpdaily_captcha_ocr_fp16.onnx` | 1.07 MB | fp16-stored, fp32-compute (lossless, recommended) / fp16 存储 fp32 计算, 无损, 推荐部署 |
+| `charset.json` | — | Character table, index 0 = CTC blank / 字符表, index 0 为 CTC blank |
+| `config.json` | — | Input size, preprocessing, decode info / 输入尺寸、预处理、解码信息 |
+
+> The fp16 file stores weights as fp16 with `Cast(fp16→fp32)` nodes; inference engines
+> constant-fold them at optimization time, so **computation stays fp32 (no accuracy loss)**
+> while the file is half the size. This avoids engines that don't support fp16 compute ops
+> (GRU/Conv). Standard fp16 conversion and int8 quantization were tested and fail to load
+> in `tract` — this fp16-cast format is the compatible compression path.
+>
+> fp16 版以 fp16 存权重 + `Cast` 节点, 推理时常量折叠回 fp32 计算(精度无损), 体积砍半,
+> 且规避了部分引擎不支持 fp16/量化算子的限制。
+
+## Specs / 规格
+
+| | |
+|---|---|
+| Architecture | Depthwise-separable CNN + 2-layer BiGRU + FC, CTC decode |
+| Charset | 62 classes: `A-Z` + `a-z` + `0-9` (+1 CTC blank = 63) |
+| Input | grayscale, resized to `32 × 160`, normalized to `[0,1]` |
+| Output | `[T, 63]` log-softmax, CTC greedy decode |
+| Accuracy | 99.37% full-string (99.7% char-level) on a hand-verified validation set |
+| Size | fp32 2.24 MB / fp16 1.07 MB (lossless compression) |
+
+## Usage (onnxruntime) / 用法
+
+```python
+import json, numpy as np, onnxruntime as ort
+from PIL import Image
+
+chars = json.load(open("charset.json"))          # ["<blank>", "A", "B", ...]
+sess = ort.InferenceSession("cpdaily_captcha_ocr_fp16.onnx",
+                            providers=["CPUExecutionProvider"])
+inp = sess.get_inputs()[0].name
+
+def recognize(path):
+    img = Image.open(path).convert("L").resize((160, 32), Image.BILINEAR)
+    x = (np.asarray(img, dtype=np.float32) / 255.0)[None, None, :, :]
+    logits = sess.run(None, {inp: x})[0][0]       # [T, 63]
+    idx = logits.argmax(-1)
+    out, prev = [], -1
+    for p in idx:                                  # CTC greedy: dedup + drop blank
+        if p != prev and p != 0:
+            out.append(chars[p])
+        prev = p
+    return "".join(out)
+
+print(recognize("captcha.png"))
+```
+
+## Usage (Rust / tract) / 用法
+
+```rust
+use tract_onnx::prelude::*;
+
+let model = tract_onnx::onnx()
+    .model_for_path("cpdaily_captcha_ocr_fp16.onnx")?
+    .with_input_fact(0, InferenceFact::dt_shape(f32::datum_type(), tvec!(1, 1, 32, 160)))?
+    .into_optimized()?
+    .into_runnable()?;
+// preprocess to [1,1,32,160] f32 in [0,1], run, then CTC-greedy decode the [T,63] output.
+```
+
+## License
+
+MIT. Trained from scratch on self-collected data.

charset.json

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+["<blank>", "A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L", "M", "N", "O", "P", "Q", "R", "S", "T", "U", "V", "W", "X", "Y", "Z", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9"]

config.json

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+{
+  "img_h": 32,
+  "img_w": 160,
+  "num_classes": 63,
+  "charset": "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789",
+  "preprocess": "grayscale, resize to 32x160, /255.0",
+  "blank_idx": 0,
+  "decode": "CTC greedy",
+  "hidden": 128,
+  "acc": 0.9937106918238994
+}

cpdaily_captcha_ocr.onnx

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+size 2239876

cpdaily_captcha_ocr_fp16.onnx

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