Legibility and Aesthetic Analysis of Handwriting

• Main Authors: Adak, Chandranath, Chaudhuri, Bidyut B., Blumenstein, Michael
• Format: Conference Proceeding
• Language: English
• Subjects: Aesthetic quality; BLSTM; CNN; Feature extraction; Handwriting; Legibility evaluation; Recurrent neural networks; Sorting; Support vector machines; SVM; Task analysis; Text analysis; Writing
• ISSN: 2379-2140
• DOI: 10.1109/ICDAR.2017.37

This paper deals with computer-based cognitive analysis towards legibility and aesthetics of a handwritten document. The legible text creates a human perception that the writing can be read effortlessly because of its orthographic clarity. The aesthetic property relates to the beautiful appearance of a handwritten document. In this study, we deal with these properties on offline Bengali handwriting. We formulate both legibility and aesthetic analysis tasks as machine learning problems supervised by the human cognitive system. We employ automatically derived feature-based recurrent neural networks to investigate writing legibility. For aesthetics evaluation, we employ hand-crafted feature-based support vector machines (SVMs). We have collected contemporary Bengali handwritings, on which the subjective legibility and aesthetic scores are provided by human readers. On this corpus containing legibility and aesthetic ground-truth information, we executed our experiments. The experimental results obtained on various handwritings are encouraging.