Representing text as abstract images enables image classifiers to also simultaneously classify text
TABLE V: Comparison of alternate string-map layouts. Each row shows the highest F1 result obtained for that string-map layout.
['String-map layout', '[¯ [ITALIC] p; ¯ [ITALIC] l]', 'Recall', 'Precision', 'F1']
For each alternative string-map, we ran the algorithm multiple times using different settings of the comparison probability threshold (¯p) and linking proportion threshold (¯l), and only show results from the run which produced the highest F1 score. Results obtained from each of the alternative string-maps are quite similar to those obtained using the heuristically-determined layout (F1 scores range from 98.99% to 99.09%). This suggests that our method of converting text into abstract image representations facilitates robust feature learning for several alternative choices of string-map structure, such as randomised string-map character order and/or layout, heuristic order and/or layout, different string-map sizes, and the inclusion/exclusion of a blue channel for leading bi-grams.