{'fraction_non_alphanumeric': 0.02305721605465414, 'fraction_numerical': 0.0, 'mean_word_length': 5.584269662921348, '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}}
Offline signature authentication using cross-validated graph matching

The biometric system is used to identify a person depending on his physiological or behavioral characteristics. Signature verification is a commonly accepted biometric method and is widely used for banking transactions. In this paper, we propose Offline Signature Authentication using Cross-validated Graph Matching (OSACGM) algorithm. The signatures are pre-processed in which signature extraction method is used to obtain high resolution for smaller normalization box. The similarity measure between two signatures in the database is determined by (i) constructing a bipartite graph G, (ii) obtaining complete matching in G and (iii) finding minimum Euclidean distance by Hungarian method. An optimum decision threshold value is determined using Cross-validation technique to select reference signatures. The test feature is extracted from the given test signature by pre-processing. Then the test feature is compared with the threshold value to authenticate the test signature. Compared to the existing algorithm, our algorithm gives better Equal Error Rate (EER) for skilled and random forgeries.