  We present a CNN-BiLSTM system for the problem of offline English handwriting
recognition, with extensive evaluations on the public IAM dataset, including
the effects of model size, data augmentation and the lexicon. Our best model
achieves 3.59\% CER and 9.44\% WER using CNN-BiLSTM network with CTC layer.
Test time augmentation with rotation and shear transformations applied to the
input image, is proposed to increase recognition of difficult cases and found
to reduce the word error rate by 2.5\% points. We also conduct an error
analysis of our proposed method on IAM dataset, show hard cases of handwriting
images and explore samples with erroneous labels. We provide our source code as
public-domain, to foster further research to encourage scientific
reproducibility.
