@mastersthesis{Rawat2023,
  type      = {Master Thesis},
  author    = {Rawat, Amit Kumar},
  title     = {Automatic Product Identification Using Deep Learning},
  institution = {Fakult{\"a}t Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019)},
  school    = {Hochschule Offenburg},
  pages     = {76},
  year      = {2023},
  abstract  = {In the past ten years, applications of artificial neural networks have changed dramatically. outperforming earlier predictions in domains like robotics, computer vision, natural language processing, healthcare, and finance. Future research and advancements in CNN architectures, Algorithms and applications are expected to revolutionize various industries and daily life further. Our task is to find current products that resemble the given product image and description. Deep learning-based automatic product identification is a multi-step process that starts with data collection and continues with model training, deployment, and continuous improvement. The caliber and variety of the dataset, the design selected, and ongoing testing and improvement all affect the model's effectiveness. We achieved 81.47\% training accuracy and 72.43\% validation accuracy for our combined text and image classification model. Additionally, we have discussed the outcomes from the other dataset and numerous methods for creating an appropriate model.},
  subject      = {Deep learning},
  language  = {en}
}