{'fraction_non_alphanumeric': 0.029473684210526315, 'fraction_numerical': 0.007368421052631579, 'mean_word_length': 4.94375, '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}}
Security Enhancement of Smart Home System using Signature Recognition on Raspberry Pi

Signatures play a crucial role in human life as part of their identity. Nowadays, there is a growing interest in the smart home system using the Internet of Things (IoT). Furthermore, signature recognition and verification can play essential roles in finance, banking, home system, insurance, and others. This paper’s main objective is to design and implement a signature recognition system on a single board computer, i.e., Raspberry Pi 3 equipped with an LCD touchscreen. First, the acquired signature image was cropped and resized. Next, a binary image was extracted as features to train the artificial neural network (ANN). The trained ANN was used to classify the input signature to determine whether the signature is genuine or forged. Results showed that the recognition rate of 99.77% was achieved using a confidence level threshold of 85% during testing.