| ---
|
| license: apache-2.0
|
| language:
|
| - en
|
| library_name: paddlepaddle
|
| tags: [anpr, ocr, license-plate, indian-plates, PP-OCRv5]
|
| ---
|
|
|
| # Data-Intelligent ANPR: Scalable License Plate Recognition Under Real-World Data Constraints
|
|
|
| ## Abstract
|
|
|
| This release provides Awiros-ANPR-OCR, a 37M-parameter specialist model for
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| Automatic Number Plate Recognition (ANPR) on Indian license plates. The model
|
| is built on the PP-OCRv5 encoder-decoder backbone (SVTR_HGNet with PPHGNetV2_B4)
|
| and fine-tuned on a curated 558,767-sample corpus spanning both standard
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| single-row and non-standard dual-row Indian plate formats.
|
|
|
| Starting from only 6,839 publicly available labeled samples, the training
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| corpus was grown through a data engineering pipeline combining synthetic data
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| synthesis, consensus pseudo-labeling, distribution-aware curation, VLM-assisted
|
| data cleanup, and state-balanced batch sampling. The resulting model achieves
|
| **98.42% accuracy** with **sub-6ms on-device inference** on an NVIDIA RTX 3090
|
| --- a 1,260x latency advantage over frontier multimodal models like Gemini.
|
|
|
| For the full data curation and training methodology, refer to our technical
|
| report: [Technical Report](TechnicalReport.pdf).
|
|
|
| ## Evaluation and Results
|
|
|
| All systems were evaluated on a shared held-out validation set constructed
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| using a distribution-aware split covering all Indian state codes, including
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| both standard and non-standard plate formats.
|
|
|
| | System | Params | Overall Acc. | 1-Row Acc. | 2-Row Acc. | Latency Avg (ms) | Throughput (img/s) |
|
| | --- | --- | --- | --- | --- | --- | --- |
|
| | **Awiros-ANPR-OCR (Ours)** | **37.3M** | **98.42%** | **98.83%** | **96.91%** | **5.09** | **196.5** |
|
| | Gemini-3-flash-preview | ~5-10B | 93.89% | 94.70% | 91.20% | 6,430 | 0.2 |
|
| | Gemini-2.5-flash-preview | ~5B | 87.23% | 89.66% | 78.38% | --- | --- |
|
| | Tencent HunyuanOCR | 996M | 67.62% | 76.65% | 34.78% | 309.15 | 3.2 |
|
| | PP-OCRv5 Pretrained | 53.6M | 57.96% | 73.55% | 0.24% | 5.25 | 190.6 |
|
|
|
| Latency measured on a single NVIDIA RTX 3090 GPU (batch size 1). Gemini
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| latency is end-to-end API round-trip. PP-OCRv5 Pretrained shares the same
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| architecture but uses original pretrained weights without domain-specific
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| fine-tuning --- the 57.96% to 98.42% gap is entirely a data story.
|
|
|
| ## Qualitative Comparison
|
|
|
| Representative samples where Awiros-ANPR-OCR correctly transcribes the plate |
| while all baselines produce errors. Common failure modes for baselines include |
| confusing visually similar characters (Q→0, V→Y, M→R, B→8) and truncating |
| dual-row plates. |
|
|
| | Plate Image | Ground Truth | Awiros (Ours) | Gemini 3 | Gemini 2.5 | Tencent | |
| | --- | --- | --- | --- | --- | --- | |
| |  | `UP14BQ9208` | `UP14BQ9208` | `UP14B09208` | `UP14B09208` | `UP14B` | |
| |  | `HR35M2576` | `HR35M2576` | `HR35R2576` | `HR35R2576` | `HR35K2576` | |
| |  | `HR34M4007` | `HR34M4007` | `HR34H4007` | `HR34M40D7` | `HR36M4007` | |
| |  | `HR51BV8223` | `HR51BV8223` | `HR51BY8223` | `HR51BY8223` | `HR51BY8223` | |
| |  | `HR38AB2421` | `HR38AB2421` | `HR38A8242` | `HR38A82421` | `HR38A` | |
| |  | `HR12AX8522` | `HR12AX8522` | `HR12AX0522` | `HR12AX0522` | `HR12AX0522` | |
| |  | `HR46E0227` | `HR46E0227` | `HR26E0227` | `HR26E0227` | `HR6E0227` | |
|
|
| Recurring character confusions across baselines: `Q→0`, `M→R/K/H`, `V→Y`, |
| `B→8`, `8→0`, `4→2`. Tencent also truncates several dual-row and |
| low-contrast plates. |
|
|
| ## Key Design Decisions
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|
|
| - **End-to-end architecture**: Eliminates brittle multi-stage pre-processing
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| pipelines (perspective normalization, row segmentation, per-region
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| recognition) that prior systems relied upon
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| - **Consensus pseudo-labeling**: Two independently trained models must agree on
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| a transcription before it is accepted as a label, substantially reducing
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| pseudo-label noise
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| - **Distribution-aware curation**: Non-linear bucket-wise train/val splits
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| ensure rare state codes are not lost to validation
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| - **State-balanced batch sampling**: Uniform state-code sampling within each
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| batch prevents training dynamics from being dominated by high-frequency states
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| - **Negative sample training**: Unreadable plates labeled with an abstention
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| token suppress hallucination on degraded inputs
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|
|
| ## Model Inference
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|
|
| Use the official PaddleOCR repository to run single-image inference with this
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| release model.
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|
|
| 1. Clone PaddleOCR and move into the repository root.
|
| ```bash
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| git clone https://github.com/PaddlePaddle/PaddleOCR.git
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| cd PaddleOCR
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| ```
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| 2. Install dependencies.
|
| ```bash
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| pip install paddlepaddle # or paddlepaddle-gpu
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| pip install safetensors pillow opencv-python pyyaml
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| ```
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| 3. Copy `test.py` and `en_dict.txt` from this release folder into the
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| PaddleOCR repository root.
|
| 4. Place `model.safetensors` in the PaddleOCR repository root (or specify the
|
| path via `--weights`).
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| 5. Run inference on a single image.
|
| ```bash
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| python test.py \
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| --image_path path/to/plate_crop.jpg \
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| --weights model.safetensors \
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| --device gpu
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| ```
|
| 6. Run inference on a directory of images.
|
| ```bash
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| python test.py \
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| --image_path path/to/plate_crops/ \
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| --weights model.safetensors \
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| --device gpu \
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| --output_json results.json
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| ```
|
|
|
| ## Architecture Details
|
|
|
| | Component | Value |
|
| | --- | --- |
|
| | Framework | PaddlePaddle / PP-OCRv5 |
|
| | Backbone | PPHGNetV2_B4 |
|
| | Head | MultiHead (CTCHead + NRTRHead) |
|
| | Input shape | 3 x 48 x 320 |
|
| | Character set | 0-9, A-Z, a-z, space (63 classes) |
|
| | Max text length | 25 |
|
| | Parameters | 37.3M |
|
| | Export format | SafeTensors (from PaddlePaddle params) |
|
|
|
| ## Summary
|
|
|
| We present a practical, data-centric ANPR framework that achieves
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| production-grade accuracy on Indian license plates without reliance on large
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| manually annotated datasets or frontier model scale. The same PP-OCRv5
|
| architecture scores 57.96% out-of-the-box and 98.42% after our data
|
| engineering pipeline --- demonstrating that the data, not the model, is the |
| primary driver of performance in domain-specific OCR. |
| |
| Users who want to test their own models on our validation set can do so in our |
| [Hugging Face Space](https://huggingface.co/spaces/uv124/license-plate-ocr-benchmark). |
| Support for submitting `.bin` files for testing in our internal systems will be |
| added soon, and the link for that submission flow will be updated shortly. |
| |
