{'fraction_non_alphanumeric': 0.021827000808407437, 'fraction_numerical': 0.0, 'mean_word_length': 5.252525252525253, '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}}
A One-Pass Approach for Slope and Slant Estimation of Tri-Script Handwritten Words

Abstract Handwritten words can never complement printed words because the former are mostly written in either skewed or slanted form or in both. This very nature of handwriting adds a huge overhead when converting word images into machine-editable format through an optical character recognition system. Therefore, slope and slant corrections are considered as the fundamental pre-processing tasks in handwritten word recognition. For solving this, researchers have followed a two-pass approach where the slope of the word is corrected first and then slant correction is carried out subsequently, thus making the system computationally expensive. To address this issue, we propose a novel one-pass method, based on fitting an oblique ellipse over the word images, to estimate both the slope and slant angles of the same. Furthermore, we have developed three databases considering word images of three popular scripts used in India, namely Bangla, Devanagari, and Roman, along with ground truth information. The experimental results revealed the effectiveness of the proposed method over some state-of-the-art methods used for the aforementioned problem.