{'fraction_non_alphanumeric': 0.018543046357615896, 'fraction_numerical': 0.005298013245033113, 'mean_word_length': 5.24793388429752, '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}}
The image Text Recognition Graph (iTRG)

This paper presents a graph based scheme for color text recognition in images and videos, which is particularly robust to complex background, low resolution or video coding artifacts. This scheme is based on a novel method named the image Text Recognition Graph (iTRG) composed of five main modules: an image text segmentation module, a graph connection builder module, a character recognition module, a graph weight calculator module and an optimal path search module. The first two modules are based on convolutional neural networks so that the proposed system automatically learns how to robustly perform segmentation and recognition. The proposed method is evaluated on the public ICDAR 2003 test word dataset.