{'fraction_non_alphanumeric': 0.0076481835564053535, 'fraction_numerical': 0.0, 'mean_word_length': 4.822222222222222, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
TDNN with masked inputs

A novel architecture based on a time delay neural network is proposed in this paper. The main idea is the introduction of selection masks which allow to specify neurons to a subset of features included in their receptive field. It turns out that the number of free parameters can be decreased while the performances of recognition remains very high. This architecture has been designed for embedded online handwriting recognition systems where memory capacity has to be reduced as much as possible.