{'fraction_non_alphanumeric': 0.015472312703583062, 'fraction_numerical': 0.0, 'mean_word_length': 5.43455497382199, '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}}
Semantic and Interaction: When Document Image Analysis Meets Computer Vision and Machine Learning

Researchers in the DIA field have proposed different interesting techniques for the extraction of elements of contents inside images at different levels: lexical, syntactical, semantical. Besides, different knowledge representation techniques have been involved for the analysis of relations between them. From these observations, it seems very interesting to propose a comparative study showing the increasing similarity between CV and DIA problems and discussing the recent trends that have been or can be proposed in a short future to try to deal more efficiently with semantic and user interaction in these fields (computer vision and robust reading). By selecting and explaining interesting recent works from the CV and DIA communities, this paper tries to bring answers to the following questions: Can we do more than automatic features selection with CNN based architectures? Can we reach a more semantical point of view on the data? What are the solutions to define adaptable semantic models that can be easily learned or user-defined? How can we get more generic systems by combining machine learning & user interaction?