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README.md

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----
-license: apache-2.0
----
+🧠 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).
+
+* * *
+
+📦 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 cek_model_v6.py \
+      --weights /workspace/captcha_final.weights.h5 \
+      --image   /workspace/dataset_500/style7/K9NO2.png
+    
+
+Optional ground truth override:
+
+    python3 cek_model_v6.py \
+      --weights /workspace/captcha_final.weights.h5 \
+      --image   /workspace/dataset_500/style7/K9NO2.png \
+      --gt      K9NO2
+    
+
+### 2) Batch from a dataset
+
+    python3 cek_model_v6.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")

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captcha_requirements.txt

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+absl-py==2.3.1
+asttokens==3.0.0
+astunparse==1.6.3
+certifi==2025.8.3
+charset-normalizer==3.4.3
+comm==0.2.3
+contourpy==1.3.3
+cycler==0.12.1
+debugpy==1.8.16
+decorator==5.2.1
+executing==2.2.1
+filelock==3.19.1
+flatbuffers==25.9.23
+fonttools==4.60.1
+fsspec==2025.9.0
+gast==0.6.0
+google-pasta==0.2.0
+grpcio==1.76.0
+h5py==3.14.0
+hf-xet==1.1.10
+huggingface-hub==0.34.4
+idna==3.10
+inquirerpy==0.3.4
+ipykernel==6.30.1
+ipython==9.5.0
+ipython_pygments_lexers==1.1.1
+ipywidgets==8.1.7
+jedi==0.19.2
+Jinja2==3.1.4
+joblib==1.5.2
+jupyter_client==8.6.3
+jupyter_core==5.8.1
+jupyterlab_widgets==3.0.15
+keras==3.12.0
+keras-nightly==3.12.0.dev2025100703
+kiwisolver==1.4.9
+libclang==18.1.1
+Markdown==3.9
+markdown-it-py==4.0.0
+MarkupSafe==2.1.5
+matplotlib==3.10.7
+matplotlib-inline==0.1.7
+mdurl==0.1.2
+ml_dtypes==0.5.3
+mpmath==1.3.0
+namex==0.1.0
+nest-asyncio==1.6.0
+networkx==3.3
+numpy==1.26.4
+nvidia-cublas-cu12==12.8.4.1
+nvidia-cuda-cupti-cu12==12.8.90
+nvidia-cuda-nvcc-cu12==12.9.86
+nvidia-cuda-nvrtc-cu12==12.8.93
+nvidia-cuda-runtime-cu12==12.8.90
+nvidia-cudnn-cu12==9.10.2.21
+nvidia-cufft-cu12==11.3.3.83
+nvidia-cufile-cu12==1.13.1.3
+nvidia-curand-cu12==10.3.9.90
+nvidia-cusolver-cu12==11.7.3.90
+nvidia-cusparse-cu12==12.5.8.93
+nvidia-cusparselt-cu12==0.7.1
+nvidia-nccl-cu12==2.28.7
+nvidia-nvjitlink-cu12==12.8.93
+nvidia-nvshmem-cu12==3.3.20
+nvidia-nvtx-cu12==12.8.90
+opt_einsum==3.4.0
+optree==0.17.0
+packaging==25.0
+pandas==2.3.3
+parso==0.8.5
+pexpect==4.9.0
+pfzy==0.3.4
+pillow==12.0.0
+platformdirs==4.4.0
+prompt_toolkit==3.0.52
+protobuf==6.33.0
+psutil==7.0.0
+ptyprocess==0.7.0
+pure_eval==0.2.3
+Pygments==2.19.2
+pyparsing==3.2.5
+python-dateutil==2.9.0.post0
+pytz==2025.2
+PyYAML==6.0.2
+pyzmq==27.1.0
+requests==2.32.5
+rich==14.2.0
+scikit-learn==1.7.2
+scipy==1.16.3
+setuptools==80.9.0
+six==1.17.0
+stack-data==0.6.3
+sympy==1.13.3
+tb-nightly==2.20.0a20250717
+tensorboard==2.20.0
+tensorboard-data-server==0.7.2
+termcolor==3.2.0
+tf_nightly==2.21.0.dev20251017
+threadpoolctl==3.6.0
+torch==2.8.0
+torchaudio==2.8.0+cu129
+torchvision==0.23.0
+tornado==6.5.2
+tqdm==4.67.1
+traitlets==5.14.3
+triton==3.4.0
+typing_extensions==4.15.0
+tzdata==2025.2
+urllib3==2.5.0
+wcwidth==0.2.13
+Werkzeug==3.1.3
+wheel==0.45.1
+widgetsnbextension==4.0.14
+wrapt==2.0.0