README.md

---
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")