A robust handwritten recognition system for learning on different data restriction scenarios

Abstract

[EN] Handwritten Text Recognition (HTR) systems have gained interest in fields of academic research and commercial applications. Deep learning techniques, and more precisely Convolutional Neural Networks (CNNs), have enabled many recent successes in the computer vision community. However, due to high computational costs, applying CNNs to many real applications is challenging since the specific training data is restricted in many cases. Therefore, in this paper, we present a Gated-CNN-BGRU optical model capable of dealing with this complex challenge. The proposed model was evaluated on five well-known datasets in HTR (Bentham, IAM, RIMES, Saint Gall, and Washington). Additionally, we redefine the training and validation partitions for each dataset, progressively varying the percentage of data between both partitions to create a total of 50 scenarios with different data volumes. The experiment validates and shows that the proposed model presents statistically significant results, surpassing the current models by an average of 2.96 and 8.91 percentage points in character and word recognition accuracy. In the most complex scenario of using 49 images for training, we achieved character and word precision of 87.25% and 71.54% respectively. That means an improvement of 78.32 and 53.54 percentage points, respectively, of the state-of-the-art optical models.