Best Practices for a Handwritten Text Recognition System
George Retsinas Giorgos Sfikas Basilis Gatos Christophoros Nikou
Handwritten text recognition has been developed rapidly in the recent years,
following the rise of deep learning and its applications. Though deep learning
methods provide notable boost in performance concerning text recognition,
non-trivial deviation in performance can be detected even when small
pre-processing or architectural/optimization elements are changed. This work
follows a ``best practice'' rationale; highlight simple yet effective empirical
practices that can further help training and provide well-performing
handwritten text recognition systems. Specifically, we considered three basic
aspects of a deep HTR system and we proposed simple yet effective solutions: 1)
retain the aspect ratio of the images in the preprocessing step, 2) use
max-pooling for converting the 3D feature map of CNN output into a sequence of
features and 3) assist the training procedure via an additional CTC loss which
acts as a shortcut on the max-pooled sequential features. Using these proposed
simple modifications, one can attain close to state-of-the-art results, while
considering a basic convolutional-recurrent (CNN+LSTM) architecture, for both
IAM and RIMES datasets. Code is available at
https://github.com/georgeretsi/HTR-best-practices/.