Deep Neural Networks Combined with STN for Multi-Oriented Text Detection and Recognition

Developing systems for interpreting visuals, such as images, videos is really challenging but important task to be developed and applied on benchmark datasets. This study solves the very challenge by using STN-OCR model consisting of deep neural networks (DNN) and Spatial Transformer Networks (STNs). The network architecture of this study consists of two stages: localization network and recognition network. In the localization network it finds and localizes text regions and generates sampling grid. Whereas, in the recognition network, text regions will be input and then this network learns to recognize text including low resolution, curved and multi-oriented text. Deep learning-based approaches require a lot of data for training effectively, therefore, this study has used two benchmark datasets, Street View House Numbers (SVHN) and International Conference on Document Analysis and Recognition (ICDAR) 2015 to evaluate the system. The STN-OCR model achieves better results than literature on these datasets.

@article{Katper2020,
  title     = {Deep Neural Networks Combined with STN for Multi-Oriented Text Detection and Recognition},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {4},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Saif Hassan Katper and Abdul Rehman Gilal and Abdullah Alshanqiti and Ahmad Waqas and Aeshah Alsughayyir and Jafreezal Jaafar},
  doi       = {10.14569/IJACSA.2020.0110424},
  url       = {https://doi.org/10.14569/IJACSA.2020.0110424}
}