{'fraction_non_alphanumeric': 0.011904761904761904, 'fraction_numerical': 0.001984126984126984, 'mean_word_length': 5.846153846153846, '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}}
Stroke frequency descriptors for handwriting-based writer identification

Writer identification in offline handwritten documents is a difficult task with multiple applications such as authentication, identification, and clustering in document collections. For example, in the context of content-based document image retrieval, given a document with handwritten annotations it is possible to determine whether the comments were added by a specific individual and find other documents annotated by the same person. In contrast to online writer identification in which temporal stroke information is available, such information is not readily available in offline writer identification. The base approach and the main contribution of our work is the idea of using derived canonical stroke frequency descriptors from handwritten text to identify writers. We show that a relatively small set of canonical strokes can be successfully employed for generating discriminative frequency descriptors. Moreover, we show that by using frequency descriptors alone it is possible to perform writer identification with success rate which is comparable to the known state of the art in offline writer identification with close to 90% accuracy. As frequency descriptors are independent of existing descriptors, the performance of offline writer identification may be improved by combining both standard and frequency descriptors. Experimental evaluation with quantitative performance evaluation is provided using the IAM dataset.1