The document presents a study on online and offline handwritten Chinese character recognition. A deep learning model is proposed that incorporates directional decomposition of characters into an 8-direction feature map. This model achieves state-of-the-art results on benchmark datasets while using less memory than comparison methods. The model can also be adapted through an unsupervised adaptation layer to new domains without requiring large labeled datasets.

1. Online and offline handwritten Chinese character recognition:
A comprehensive study and new benchmark
Xu-Yao Zhang, Yoshua Bengio, Cheng-Lin Liu
Presenter: Shuhei Iitsuka

2. Handwritten character recognition
Insight: Deep learning approach is becoming dominant in this field, but accuracy can be improved by
incorporating well-studied domain knowledge — direction decomposition — to the network.
Offline recognition: classification from still image.
Online recognition: classification from stokes.
Direction decomposition with 8 directinos.
Extracted features are called “directMap”.
For online data, put weight of 0.5 on
off-strokes to get enhanced
representation.

3. Proposed Method
• Instead of inputting pixel-based images, the model
accepts directMap (8 direction x 32 width x 32
height).
• Outputs 3755 character classification.
• CNN with 11 layers
• Dropout rate increases gradually but the last layer.
– The top layer is propergate signals with high
dropout rate.
• Reduce the size of feature map slowly with
max-pooling layers.

4. Adaptation
In order to transfer models to new domains, it uses
“adaptation layer” on the last layer.
“A widely used strategy is to train or fine-tune a state-of the art deep
model on a new domain, but this requires a significant amount of
labeled data. A better solution is the domain adaptation of the deep
models.”
This adaptation layer is learned in unsupervised manner.
▶ Efficiently implemented on applications (as long as the
domains share the same output layer).

5. Experiments (offline)
Proposed method outperformed
the state-of-the-art.
Memory consumption is much smaller,
which is an advantage for application.

6. Experiments (online)
Similar tendency as online is observed.

7. Effect of decaying learning rate
• When the cost or accuracy is not
improving, drop the learning rate by x0.3.
• With this simple technique, the accuracy is
improved hugely by escaping from the
plateau.