updated


README.md

@@ -1,49 +1,112 @@
 ---
-base_model: microsoft/trocr-base-handwritten
+language:
+- en
+license: mit
+library_name: transformers
 tags:
-- generated_from_trainer
+- image-to-text
+- license-plate-recognition
+- ocr
+- transformers
+datasets:
+- PawanKrGunjan/license_plates
+metrics:
+- cer
+base_model: microsoft/trocr-base-handwritten
 model-index:
 - name: license_plate_recognizer
-  results: []
+  results:
+  - task:
+      type: image-to-text
+      name: License Plate Recognition
+    dataset:
+      type: PawanKrGunjan/license_plates
+      name: License Plates Dataset
+      config: default
+      split: validation
+    metrics:
+    - type: cer
+      value: 0.0036
+      name: Character Error Rate (CER)
+pipeline_tag: image-to-text
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+# License Plate Recognizer
+
+This model is a fine-tuned version of the [microsoft/trocr-base-handwritten](https://huggingface.co/microsoft/trocr-base-handwritten) model, specifically designed for recognizing and extracting text from license plate images. It was trained on the [PawanKrGunjan/license_plates](https://huggingface.co/datasets/PawanKrGunjan/license_plates) dataset and is ideal for OCR tasks focused on license plates.
+
+## Model Description
+
+### TrOCR (base-sized model, fine-tuned on IAM)
+
+This model is based on the TrOCR model, which was introduced in the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Li et al. The original TrOCR model was first released in [this repository](https://github.com/microsoft/unilm/tree/master/trocr).
+
+The TrOCR model utilizes an encoder-decoder architecture:
+- **Encoder:** A Transformer-based image encoder initialized from BEiT weights.
+- **Decoder:** A Transformer-based text decoder initialized from RoBERTa weights.
+
+The model processes images as sequences of patches (16x16 resolution) and generates text autoregressively.
+
+This version of TrOCR has been fine-tuned on the IAM dataset for improved performance in OCR tasks involving handwritten text, making it particularly effective for recognizing text in license plates.
+
+### Fine-Tuning Details
+
+- **Base Model:** The model was fine-tuned from .[microsoft/trocr-base-handwritten](https://huggingface.co/microsoft/trocr-base-handwritten) model.
+- **Dataset:** Fine-tuning was performed using the [PawanKrGunjan/license_plates](https://huggingface.co/datasets/PawanKrGunjan/license_plates) dataset.
+
+## Intended Uses & Limitations
+
+### Use Cases
+
+- **License Plate Recognition:** Extract text from license plate images for use in various automated systems.
+- **Automated Surveillance:** Suitable for integration into automated surveillance systems for real-time monitoring.
+
+### Limitations
+
+- **Environmental Constraints:** Performance may degrade in low-light conditions or with low-resolution images.
+- **Regional Variability:** The model may struggle with license plate designs that differ significantly from those in the training dataset.
 
-[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/pawankrgunjan/huggingface/runs/3caod3mu)
-# license_plate_recognizer
+# How to Use
 
-This model is a fine-tuned version of [microsoft/trocr-base-handwritten](https://huggingface.co/microsoft/trocr-base-handwritten) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.0098
-- Cer: 0.0053
+Here’s an example of how to use the model in Python:
 
-## Model description
+```python
+from transformers import TrOCRProcessor, VisionEncoderDecoderModel
+from PIL import Image
+import requests
 
-More information needed
+# Load the processor and model
+processor = TrOCRProcessor.from_pretrained("your-username/license_plate_recognizer")
+model = VisionEncoderDecoderModel.from_pretrained("your-username/license_plate_recognizer")
 
-## Intended uses & limitations
+# Load an image of a license plate
+image_url = "https://datasets-server.huggingface.co/assets/PawanKrGunjan/license_plates/--/c1a289cb616808b2a834fae90d9625c2f78b82c9/--/default/train/34/image/image.jpg?Expires=1723689029&Signature=jlu~8q7l2MT2IhbS5UttYLkPaMX3416a9CByGBa9M5QKNqi9ezSTYLkDsliKKgO2c-TbiJ8LsEAOB8jmcXwQkN6eNBjrJpnyGqBZ7T99P-cXk5XwHiJa27bn6jINvBUBVID8ganhqBv-DubyyM4RcksxyjZNAE7yatBTBbaDk1-mno5pbL7fpFb~gHfMvMGalPWa-vO3teeoS0yHhp5yNzSjObmwzqn42bZpCFA3dleRPnzikyKPR3OzFK1BaPyr2bzJsLUlg3H7E8c3NGz~ryLjBREa2KpyM2X0JkhzvT0fEGsdaiyN36Tkqoi2aeH~KM8YzztD7W-jSH83dckdxw__&Key-Pair-Id=K3EI6M078Z3AC3"
+image = Image.open(requests.get(image_url, stream=True).raw)
 
-More information needed
+# Process the image
+pixel_values = processor(image, return_tensors="pt").pixel_values
 
-## Training and evaluation data
+# Generate the text prediction
+generated_ids = model.generate(pixel_values)
+generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
 
-More information needed
+print(generated_text)
+```
 
-## Training procedure
+# Training procedure
 
-### Training hyperparameters
+## 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: 20
+- **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**: 20
 
-### Training results
+## Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Cer    |
 |:-------------:|:-----:|:----:|:---------------:|:------:|
@@ -69,9 +132,9 @@ The following hyperparameters were used during training:
 | 0.0001        | 20.0  | 7940 | 0.0047          | 0.0018 |
 
 
-### Framework versions
+## Framework versions
 
 - Transformers 4.42.3
 - Pytorch 2.1.2
 - Datasets 2.20.0
-- Tokenizers 0.19.1
+- Tokenizers 0.19.1