{'fraction_non_alphanumeric': 0.02982292637465051, 'fraction_numerical': 0.0, 'mean_word_length': 5.392857142857143, '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}}
Implementation and Comparison of Text-Based Image Retrieval Schemes

Search engines, i.e., Google, Yahoo pro-vide various libraries and API’s to assist programmers and researchers in easier and efficient access to their collected data. When a user generates a search query, the dedicated Application Programming Interface (API) returns the JavaScript Object Notation (JSON) file which contains the desired data. Scraping techniques help image descriptors to separate the image’s URL and web host’s URL in different documents for easier implementation of different algorithms. The aim of this paper is to propose a novel approach to effectively filter out the desired image(s) from the retrieved data. More specifically, this work primarily focuses on applying simple yet efficient techniques to achieve accurate image retrieval. We compare two algorithms, i.e., Cosine similarity and Sequence Matcher, to obtain the accuracy with a minimum of irrelevance. Obtained results prove Cosine similarity more accurate than its counterpart in finding the maximum relevant image(s).