metadata

base_model: microsoft/trocr-base-handwritten
tags:
  - trocr
  - image-to-text
  - license-plate-number
model-index:
  - name: license_plate_recognizer
    results:
      - task:
          type: image-to-text
          name: License Plate Recognition
        dataset:
          type: custom_dataset
          name: Custom License Plate Dataset
          config: default
          split: test
          revision: main
        metrics:
          - type: cer
            value: 0.0231
            name: Test CER
            config: default
            args:
              max_order: 4
        source:
          name: Hugging Face Model Card
          url: https://huggingface.co/PawanKrGunjan/license_plate_recognizer
license: mit
language:
  - en
metrics:
  - cer
library_name: transformers
pipeline_tag: image-to-text
datasets:
  - charliexu07/license_plates

license_plate_recognizer

This model is a fine-tuned version of microsoft/trocr-base-handwritten specifically tailored for recognizing license plate numbers from images. The fine-tuning process has been optimized to accurately decode alphanumeric characters typically found on license plates.

Model Description

The base model, microsoft/trocr-base-handwritten, is a Transformer-based OCR model designed for recognizing handwritten text. This fine-tuned version is adapted for license plate recognition, enhancing its ability to read and transcribe license plates from various sources, including images captured under different lighting and angles.

Intended Uses & Limitations

Intended Uses

License Plate Recognition: This model is designed to extract and transcribe alphanumeric characters from images of license plates. It can be used in various applications such as automated toll systems, parking management, and law enforcement.

Limitations

Character Set: The model is optimized for the specific alphanumeric characters commonly found on license plates. It may not perform well on text outside this domain.
Environmental Factors: While robust to typical variations in image quality, extreme conditions like very low light, heavy blurring, or unusual angles may reduce accuracy.

Training and Evaluation Data

The model was fine-tuned on a dataset consisting of license plate images. The dataset includes a diverse set of license plates captured in various environments and lighting conditions, ensuring robustness in real-world applications. However, specific details about the dataset (e.g., size, source) are not provided here.

Training Procedure

Training Hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9, 0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 7

Training Results

Training Loss	Epoch	Step	Validation Loss	Cer
0.2605	1.0	254	0.0798	0.0253
0.138	2.0	508	0.0660	0.0177
0.0435	3.0	762	0.0645	0.0146
0.0344	4.0	1016	0.0594	0.0173
0.011	5.0	1270	0.0626	0.0160
0.0021	6.0	1524	0.0567	0.0120
0.0007	7.0	1778	0.0599	0.0137

Final Evaluation Metrics

Loss: 0.0653
Cer: 0.0231

Certainly! Here’s the updated "How to Use the Model" section with the correct username:

How to Use the Model

Here is how you can use this fine-tuned model in PyTorch to recognize license plate numbers:

from transformers import TrOCRProcessor, VisionEncoderDecoderModel
from PIL import Image
import requests

# Load an image of a license plate
url = 'https://example.com/path/to/license_plate_image.jpg'
image = Image.open(requests.get(url, stream=True).raw).convert("RGB")

# Initialize the processor and the fine-tuned model
processor = TrOCRProcessor.from_pretrained('PawanKrGunjan/license_plate_recognizer')
model = VisionEncoderDecoderModel.from_pretrained('PawanKrGunjan/license_plate_recognizer')

# Preprocess the image
pixel_values = processor(images=image, return_tensors="pt").pixel_values

# Generate text (license plate number)
generated_ids = model.generate(pixel_values)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]

print("Recognized License Plate Number:", generated_text)

In this example:

Replace the url with the actual URL of an image containing a license plate.
The model and processor are loaded from your fine-tuned model on the Hugging Face Hub (PawanKrGunjan/license_plate_recognizer).

Framework Versions

Transformers: 4.42.3
Pytorch: 2.1.2
Datasets: 2.20.0
Tokenizers: 0.19.1