| --- |
| license: apache-2.0 |
| datasets: |
| - squirelmail/dataset-BotDetect-CAPTCHA-Generator |
| language: |
| - en |
| metrics: |
| - accuracy |
| pipeline_tag: image-text-to-text |
| library_name: keras |
| tags: |
| - ocr |
| - captcha |
| - crnn |
| - ctc |
| - tensorflow |
| - keras |
| - 50x250 |
| - uppercase |
| - digits |
| --- |
| # Model AI For Solve BotDetect-CAPTCHA-Generator Gov ID Captcha |
|
|
| 🧠 CRNN+CTC Checkpoints |
| ======================= |
|
|
| This directory contains **Keras 3** `save_weights`\-style checkpoints produced during training of a CRNN + CTC model for 5-char uppercase/digit CAPTCHA (image size `H=50`, `W=250`, grayscale). |
|
|
| * * * |
|
|
| 📁 Contents |
| ----------- |
|
|
| * `captcha_best.weights.h5` — best validation loss (auto-updated during training). |
| * `captcha_epNNN.weights.h5` — per-epoch snapshots (e.g., `captcha_ep001.weights.h5` … `captcha_ep022.weights.h5`). |
|
|
| All files are _weights only_; they must be loaded into the same model architecture used in training (the tester builds that architecture for you). |
|
|
| * * * |
|
|
| ✅ Model Result captcha_ep022.weights.h5 => 90.91% Accuracy |
| ----------- |
| |
| ``` |
| (venv) root@prod-exploit-sa-all-01:/home/infra# date && python3 cek_model_v6.py --weights captcha_ep022.weights.h5 --data-root ./dataset_1000_rand --sample 24000 && date |
| Thu Oct 30 01:12:49 WITA 2025 |
| WARNING: All log messages before absl::InitializeLog() is called are written to STDERR |
| I0000 00:00:1761761571.108235 2264160 cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used. |
| I0000 00:00:1761761571.304280 2264160 cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. |
| To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. |
| WARNING: All log messages before absl::InitializeLog() is called are written to STDERR |
| I0000 00:00:1761761575.452128 2264160 cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used. |
| Found weights: captcha_ep022.weights.h5 | size: 27757.0 KB | mtime: Thu Oct 30 01:02:51 2025 |
| E0000 00:00:1761761576.513960 2264160 cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: UNKNOWN ERROR (303) |
| TF GPUs: [] |
| OK: weights loaded. |
| Base output shape: (None, 31, 37) |
| Testing on 24000 samples from ./dataset_1000_rand ... |
| W0000 00:00:1761761611.159498 2264160 cpu_allocator_impl.cc:84] Allocation of 1200000000 exceeds 10% of free system memory. |
| 00 GT: 976VF | Pred: 976VF |
| 01 GT: 7W20H | Pred: 7W20H |
| 02 GT: UUU24 | Pred: UUU24 |
| 03 GT: 1EMVZ | Pred: 1EMVZ |
| 04 GT: WY4RD | Pred: WY4RD |
| 05 GT: 0GNKE | Pred: 0GNKE |
| 06 GT: 7Y5TY | Pred: 7Y5TY |
| 07 GT: OC8C1 | Pred: OC8C1 |
| 08 GT: 5ZIDQ | Pred: 5ZIDQ |
| 09 GT: LP8IP | Pred: LP8IP |
| 10 GT: AKQ7G | Pred: AKQ7G |
| 11 GT: X23QD | Pred: X23QD |
| |
| Exact match: 90.91% | Mean CER: 0.0194 |
| |
| Total images tested: 24000 |
| |
| Thu Oct 30 01:18:07 WITA 2025 |
| ``` |
| |
| * * * |
| |
| 📦 Requirements |
| --------------- |
|
|
| Install from the pinned list in the repo root: |
|
|
| # (recommended) fresh virtualenv |
| python3 -m venv venv |
| source venv/bin/activate |
| |
| # install exact deps |
| pip install -r captcha_requirements.txt |
| |
| |
| **Important:** Keras/TensorFlow versions should match what was used during training. If you trained with TF/Keras nightly or dev builds, test in the same environment to avoid weight-loading shape/key mismatches. |
|
|
| * * * |
|
|
| 🧪 How to Test |
| -------------- |
|
|
| The tester script re-creates the training graph (CRNN+CTC), loads the selected checkpoint, and runs inference with the _base_ (CTC-free) submodel. |
|
|
| ### 1) Single image |
|
|
| python3 check_model.py \ |
| --weights /workspace/captcha_final.weights.h5 \ |
| --image /workspace/dataset_500/style7/K9NO2.png |
| |
| |
| Optional ground truth override: |
|
|
| python3 check_model.py \ |
| --weights /workspace/captcha_final.weights.h5 \ |
| --image /workspace/dataset_500/style7/K9NO2.png \ |
| --gt K9NO2 |
| |
| |
| ### 2) Batch from a dataset |
|
|
| python3 check_model.py \ |
| --weights /home/infra/models/captcha_ep002.weights.h5 \ |
| --data-root /datasets/dataset_500 \ |
| --samples 64 |
| |
| |
| Expected directory layout for `--data-root`: |
|
|
| /datasets/dataset_500/ |
| ├── style0/ |
| │ ├── A1B2C.png |
| │ └── ... |
| ├── style1/ |
| │ └── ... |
| └── ... |
| └── style59/ |
| |
| |
| **Image format:** grayscale PNG, resized to `50x250` in the script. |
| **Labels:** derived from filename (regex `^[A-Z0-9]{5}$`). |
|
|
| * * * |
|
|
| 🧩 Model Details (for reference) |
| -------------------------------- |
|
|
| * Backbone: 3× (Conv2D + BN + MaxPool), then reshape to time-steps. |
| * RNN head: 2× BiLSTM(128), `return_sequences=True`. |
| * Classifier: Dense(`num_classes = 36 + 1`) with softmax; `+1` is the CTC blank. |
| * Time steps: width is downsampled by 8 ⇒ `250/8 = 31` time steps. |
|
|
| The tester script internally builds both: `model_with_ctc` (training graph) and `base_model` (inference). It loads weights into the training graph and then uses `base_model` for predictions. |
|
|
| * * * |
|
|
| 🎛️ CLI Options |
| --------------- |
|
|
| --weights <path> : required, *.weights.h5 (same architecture) |
| --image <path> : test a single image |
| --gt <text> : ground truth for --image (default: file name) |
| --data-root <dir> : style0..style59 folders for batch testing |
| --samples N : max number of images for batch test (default 64) |
| --height H : input height (default 50) |
| --width W : input width (default 250) |
| --ext png|jpg : image extension for batch (default png) |
| --show K : print K sample predictions (default 12) |
| |
| |
| * * * |
|
|
| 📊 Output |
| --------- |
|
|
| * Per-sample preview lines: `GT: ABC12 | Pred: ABC12` |
| * Aggregate metrics: |
| * **Exact match** (% of predictions exactly equal to GT) |
| * **Mean CER** (character error rate) |
|
|
| * * * |
|
|
| 🧯 Troubleshooting |
| ------------------ |
|
|
| * **“A total of 1 objects could not be loaded… <Dense name=predictions>”** |
| Mismatch between Keras/TF versions or model definition. Use the same environment and architecture as training. |
| * **GPU not used** |
| Ensure a CUDA-enabled TF build and matching drivers. For server-side issues, test with: |
| |
| import tensorflow as tf |
| print(tf.config.list_physical_devices('GPU')) |
| |
| * **NaN loss during training** |
| Check: label regex filtering, correct `input_length=31`, use `int32` for CTC inputs, disable LSTM dropouts when using cuDNN (set to `0.0`). |
| |
| * * * |
|
|
| 🔐 Notes |
| -------- |
|
|
| * CTC blank ID = `36` (since charset is 36 chars: 0-9 + A-Z). |
| * All checkpoints here are _weights only_; to export a full model, save the base model as `.keras` after loading weights in the same environment: |
| |
| model_with_ctc, base_model = build_models(...) |
| model_with_ctc.load_weights("captcha_epXXX.weights.h5") |
| base_model.save("captcha_epXXX_base.keras") |
