Hieroglyphs-Detection-And-Classification

Aim: Detecting and Classifying Hieroglyphs using CNN and Logistic Regression Steps

• Phase 1

  • Reading dataset and performing label encoding on the labels
  • Splitting the dataset into training and testing
  • Using Inception V3 for extraing features
  • Using these features to train and test using Logistic Regression
  • Evaluating the model using accuracy
  • Testing model on unseen data

• Phase 2

  • Read the dataset
  • Augment the dataset
  • Label encoding the labels
  • Perform one hot encoding on the labels
  • Split into training and testing dataset
  • Develop CNN model
  • Evaluate on
    • Loss
    • Accuracy
    • Precision
    • Recall
    • F1 Score
  • Model is generalised on another dataset

Conclusion

• CNN is better performing
• Model is generalised