  In recent years, long short-term memory neural networks (LSTMs) have been
applied quite successfully to problems in handwritten text recognition.
However, their strength is more located in handling sequences of variable
length than in handling geometric variability of the image patterns.
Furthermore, the best results for LSTMs are often based on large-scale training
of an ensemble of network instances. In this paper, an end-to-end convolutional
LSTM Neural Network is used to handle both geometric variation and sequence
variability. We show that high performances can be reached on a common
benchmark set by using proper data augmentation for just five such networks
using a proper coding scheme and a proper voting scheme. The networks have
similar architectures (Convolutional Neural Network (CNN): five layers,
bidirectional LSTM (BiLSTM): three layers followed by a connectionist temporal
classification (CTC) processing step). The approach assumes differently-scaled
input images and different feature map sizes. Two datasets are used for
evaluation of the performance of our algorithm: A standard benchmark RIMES
dataset (French), and a historical handwritten dataset KdK (Dutch). Final
performance obtained for the word-recognition test of RIMES was 96.6%, a clear
improvement over other state-of-the-art approaches. On the KdK dataset, our
approach also shows good results. The proposed approach is deployed in the Monk
search engine for historical-handwriting collections.
