author = "Souza Neto, Arthur Flor de and Bezerra, Byron Leite Dantas and 
                         Toselli, Alejandro Hector and Lima, Estanislau Baptista",
          affiliation = "{Universidade de Pernambuco} and {Universidade de Pernambuco} and 
                         {Universitat Politecnica de Valencia} and {Universidade de 
                title = "HTR-Flor: a deep learning system for offline handwritten text 
            booktitle = "Proceedings...",
                 year = "2020",
               editor = "Musse, Soraia Raupp and Cesar Junior, Roberto Marcondes and 
                         Pelechano, Nuria and Wang, Zhangyang (Atlas)",
         organization = "Conference on Graphics, Patterns and Images, 33. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Handwritten Text Recognition, Gated Convolutional Neural Networks, 
                         Gated CNN, Deep Neural Networks.",
             abstract = "In recent years, Handwritten Text Recognition (HTR) has captured a 
                         lot of attention among the researchers of the computer vision 
                         community. Current state-of-the-art approaches for offline HTR are 
                         based on Convolutional Recurrent Neural Networks (CRNNs) excel at 
                         scene text recognition. Unfortunately, deep models such as CRNNs, 
                         Recurrent Neural Networks (RNNs) are likely to suffer from 
                         vanishing/exploding gradient problems when processing long text 
                         images, which are commonly found in scanned documents. Besides, 
                         they usually have millions of parameters which require huge amount 
                         of data, and computational resource. Recently, a new class of 
                         neural network architecture, called Gated Convolutional Neural 
                         Networks (Gated-CNN), has demonstrated potentials to complement 
                         CRNN methods in modeling. Therefore, in this paper, we present a 
                         new architecture for HTR, based on Gated-CNN, with fewer 
                         parameters and fewer layers, which is able to outperform the 
                         current state-of-the-art architectures for HTR. The experiment 
                         validates that the proposed model has statistically significant 
                         recognition results, surpassing previous HTR systems by an average 
                         of 33% over five important handwritten benchmark datasets. 
                         Moreover, the proposed model is able to achieve satisfactory 
                         recognition rates even in case of few training data. Finally, its 
                         compact architecture requires less computational resources, which 
                         can be applied for real-world applications that have hardware 
                         limitations, such as robots and smartphones.",
  conference-location = "Virtual",
      conference-year = "Nov. 7-10, 2020",
             language = "en",
           targetfile = "PID6607213.pdf",
        urlaccessdate = "2021, Jan. 19"