{'fraction_non_alphanumeric': 0.02265705458290422, 'fraction_numerical': 0.004119464469618949, 'mean_word_length': 5.311688311688312, '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}}
Combination of local and global vision modelling for Arabic handwritten words recognition

We propose an Arabic handwritten word recognition system based on the idea of the PERCEPTRO system developed by Cote (Cote et al. (1998)) for Latin word recognition. It is a specific neural network, named transparent neural network, combining a global and a local vision modeling (GVM-LVM) of the word. In the forward propagation movement, the former (GVM) proposes a list of structural features characterizing the presence of some letters in the word. GVM proposes a list of possible letters and words containing these characteristics. Then, in the backpropagation movement, these letters are confirmed or not according to their proximity with corresponding printed letters. The correspondence between the letter shapes and the corresponding printed letters is performed by LVM using the correspondence of their Fourier descriptors, playing the role of a letter shape normalizer.