Offline continuous handwriting recognition using sequence to sequence neural networks

Publicado en Neurocomputing


This paper proposes the use of a new neural network architecture that combines a deep convolutional neural network with an encoder–decoder, called sequence to sequence, to solve the problem of recognizing isolated handwritten words. The proposed architecture aims to identify the characters and contextualize them with their neighbors to recognize any given word. Our model proposes a novel way to extract relevant visual features from a word image. It combines the use of a horizontal sliding window, to extract image patches, and the application of the LeNet-5 convolutional architecture to identify the characters. Extracted features are modeled using a sequence-to-sequence architecture to encode the visual characteristics and then to decode the sequence of characters in the handwritten text image. We test the proposed model on two handwritten databases (IAM and RIMES) under several experiments to determine the optimal parameterization of the model. Competitive results above those presented in the current state-of-the-art, on handwriting models, are achieved. Without using any language model and with closed dictionary, we obtain a word error rate in the test set of 12.7% in IAM and 6.6% in RIMES.