{'fraction_non_alphanumeric': 0.018461538461538463, 'fraction_numerical': 0.0, 'mean_word_length': 4.951219512195122, '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}}
Jersey Number Recognition with Semi-Supervised Spatial Transformer Network

It is still a challenging task to recognize the jersey number of players on the court in soccer match videos, as the jersey numbers are very small in the object detection task and annotated data are not easy to collect. Based on the object detection results of all the players on the court, a CNN model is first introduced to classify these numbers on the deteced players' images. To localize the jersey number more precisely without involving another digit detector and extra consumption, we then improve the former network to an end-to-end framework by fusing with the spatial transformer network (STN). To further improve the accuracy, we bring extra supervision to STN and upgrade the model to a semi-supervised multi-task learning system, by labeling a small portion of the number areas in the dataset by quadrangle. Extensive experiments illustrate the effectiveness of the proposed framework.