{'fraction_non_alphanumeric': 0.023538344722854973, 'fraction_numerical': 0.0, 'mean_word_length': 5.724489795918367, '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}}
CAT-API Framework Prototypes

Traditionally, Pattern Recognition applications have focused on fully automatic systems. However, since their performance is far from being perfect, such automatic systems cannot replace the human expertise. Typically, experts use a Pattern Recognition system in a two-step operation: first, the application generates an output in a fully automatic way; and second, the user revises that output in order to achieve high-quality results. This post-edition approach is rather inefficient and uncomfortable for the user. An alternative, yet effective approach to traditional Pattern Recognition systems is the interactive-predictive paradigm in which both the system is guided by the user and the user is assisted by the system to complete their tasks as efficiently as possible. We present three protypes of Computer Assisted Tools: transcription, translation, and syntactic parsing, respectively. Such prototypes combine the efficiency of the traditional Pattern Recognition systems with the accuracy of the human expertise, enforcing all a multimodal, interactive strategy, and fully integrating the user's knowledge into the Pattern Recognition process. User feedback directly allows to improve system accuracy, while multimodality increases both system ergonomy and user acceptability.