Neural Networks for Handwriting Recognition - Computational Intelligence Paradigms in Advanced Pattern Classification - page 21

Table 5 Summarized results from the offline French handwriting recognition competition

System Word Accuracy
HMM+MLP Combination 83.17%
Non-Symmetric HMM 83.17 %

CTC (MDLSTM) 93.17%

A summary of the results appear in Tables 3-5. As can be seen, the approach de-
scribed in this chapter always outperformed the other systems in the offline case.
This observation is very promising, because the system just uses the 2-
dimensional raw pixel data as an input.
For the online competition (Table 3) a commercial recognizer performed better
than the CTC approach. However, if the CTC system would be combined with
State-of-the-Art preprocessing and feature extraction methods, it would probably
reach a higher performance. This observation has been made in [39], where ex-
tended experiments to those in Section 1.4.1 have been performed.
Having a look at the calculation time (milliseconds per text line) also reveals
very promising results. The MDLSTM combined with CTC was among the fastest
recognizers in the competitions. Using some pruning strategies could further in-
crease the recognition speed.

5 Conclusion

This chapter described a novel approach for recognizing unconstrained handwrit-
ten text, using a recurrent neural network. The key features of the network are the
bidirectional Long Short-Term Memory architecture, which provides access to
long range, bidirectional contextual information, and the Connectionist Temporal
Classification output layer, which allows the network to be trained on unseg-
mented sequence data. In experiments on online and offline handwriting data, the
new approach outperformed state-of-the-art HMM-based classifiers and several
other recognizers. We conclude that this system represents a significant advance in
the field of unconstrained handwriting recognition, and merits further research. A
toolkit implementing the presented architecture is freely available to the public.

References

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