Fast Multi-language LSTM-based Online Handwriting Recognition
Table 9: Character error rates on the validation data using successively more of the system components described above for English (en), Spanish (es), German (de), Arabic (ar), Korean (ko), Thai (th), Hindi (hi), and Chinese (zh) along with the respective number of items and characters in the test and training datasets. Average latencies for all languages and models were computed on an Intel Xeon E5-2690 CPU running at 2.6 GHz.
['Language Internal test data (per language)', 'Language Internal test data (per language)', 'en', 'es', 'de', 'ar', 'ko', 'th', 'hi', 'zh']
The new models are more accurate (Sec. We provide an evaluation of our production system trained on our in-house datasets applied to a number of publicly available benchmark datasets from the literature. Note that for all experiments presented in this section we evaluate our current live system without any tuning specific to the tasks at hand. Our in-house datasets consist of various types of training data, the amount of which varies by script. This leads to more heterogeneous datasets than academic datasets such as IBM-UB-1 or IAM-OnDB which were collected under standardized conditions. The number of training samples varies from tens of thousands to several million per script, depending on the complexity and usage. The best configuration for our system was identified by running multiple experiments over a range of layer depths and widths on our internal datasets. The largest relative improvement comes from the overall network architecture stack, followed by the use of the character language model and the other feature functions.