Deep Hybrid Fusion Model with OCR Integration for Accurate Advertisement Categorization

This project presents an innovative approach to accurate advertisement classification by developing a hybrid deep learning model. By integrating image processing, text analysis, and an OCR system, the model achieves high precision in categorizing advertisements. The project encompasses essential steps such as data collection, image preprocessing, and text extraction from advertisement images using OCR. For feature extraction, Convolutional Neural Networks (CNNs) are employed for image analysis, while either Recurrent Neural Networks (RNNs) or Transformers are used for text analysis. Through iterative training and optimization of hyperparameters, the model’s performance is maximized. During the inference phase, the model effectively classifies new advertisements by leveraging OCR for text extraction and incorporating both image and text features for accurate prediction. The project continuously refines its methodology through ongoing data collection, preprocessing enhancements, and experimentation with various architectures. This comprehensive approach holds significant potential in marketing analysis, targeted advertising, and content filtering, offering valuable insights and advancements to the field.