Fine tune trocr model

Models
1

Hi everyone,

I’m currently working on fine-tuning the TrOCR model (from Hugging Face) for a handwriting OCR task using two datasets: IAM Handwriting Database and Imgur5K. My goal is to achieve:

CER ≤ 7%

WER ≤ 15%

However, after 10 epochs of training with TrOCR Base, my best results are:

CER: 48%

WER: 68%

Clearly, I’m far from the target and can anyone hrlp me in achieve target

Here’s what I’m using:

Model: TrOCR Base

Hi everyone,

I’m currently working on fine-tuning the TrOCR model (from Hugging Face) for a handwriting OCR task using two datasets: IAM Handwriting Database and Imgur5K. My goal is to achieve:

CER ≤ 7%

WER ≤ 15%

However, after 15 epochs of training with TrOCR Base, my best results are:

CER: 48%

WER: 68%

Clearly, I’m far from the target and can anyone help

Here’s what I’m using:

Model: TrOCR Base

1 Like
2

Hmm…

by Hugging Chat: HuggingChat

To improve the performance of your TrOCR model for handwriting OCR, consider the following structured approach:

  1. Data Preprocessing and Augmentation:

    • Clean Up and Normalize: Ensure images are noise-free and normalized in size and contrast. This helps the model focus on relevant features.
    • Augmentation: Apply transformations like rotation, cropping, and noise addition to artificially expand your dataset, enhancing generalization.

  2. Training Strategy:

    • Increase Epochs: Extend training beyond 10 epochs to allow the model to learn more effectively.
    • Learning Rate and Optimizer: Experiment with different learning rates and optimizers. Consider implementing learning rate scheduling for efficiency.

  3. Model Fine-tuning:

    • Larger Models: Explore using TrOCR-large for better performance, despite higher computational costs.
    • Alternative Models: Consider fine-tuning models known for strong handwriting performance.

  4. Tokenizer Optimization:

    • Comprehensive Vocabulary: Ensure the tokenizer includes all necessary characters and introduce a special token for unknown characters.

  5. Data Balancing:

    • Check Bias: Evaluate dataset balance and address any biases, possibly through balancing techniques or adjusted loss functions.

  6. Evaluation Metrics:

    • Expand Metrics: Track additional metrics like accuracy and analyze model failures to identify patterns in errors.

  7. Cost Function and Regularization:

    • Experiment with Loss Functions: Try different loss functions and add regularization like dropout to prevent overfitting.

  8. Model Architecture Adjustments:

    • Enhance Architecture: Consider modifying the transformer layers or adding attention mechanisms for improved performance.

  9. Research and Comparison:

    • Benchmarking: Review existing research and compare your approach with others who have used TrOCR on similar tasks to identify best practices.

By systematically addressing each of these areas, starting with preprocessing and training adjustments, you can identify and mitigate performance bottlenecks, progressively improving your model’s CER and WER.