{'fraction_non_alphanumeric': 0.015302727877578177, 'fraction_numerical': 0.0, 'mean_word_length': 5.567685589519651, '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}}
Skeletal Similarity based Structural Performance Evaluation for Document Binarization

Document image binarization algorithms are usually evaluated by a pixelwise comparison. Such metrics can be misleading and do not assess the overall structure of the text in the image, thus they do not measure the character recognition capability of the binarized image. In this paper we propose the use of metrics, based on skeleton comparisons, to evaluate structural consistency of the strokes that better correspond to character readability in the binarized image. This approach divides the skeleton of two binary images to be compared (e.g. binarization result and ground truth) in small segments and measures curve similarity between those segments. We conducted experiments with manually generated data with small distortions in the image, which greatly affect common pixelwise metrics but do not hinder the readability of the text. We also binarized images of well-known document binarization datasets using classical and state-of-the-art algorithms such as Otsu's and deep learning methods. On both manually generated and real data it could be demonstrated, amongst others, that the skeletal similarity metrics are more consistent than the pixelwise comparison regarding small character distortions and better capture the readability of the binarized image. Skeletal similarity metrics can be used to complement the pixelwise comparison towards multifaceted performance evaluation for document binarization.