Handwriting Word Spotting in the Space of Difference between Representations Using Vision Transformers

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Handwriting Word Spotting in the Space of Difference between Representations Using Vision Transformers 7 Pages Posted: 19 May 2022 See all articles by Mohamed MHIRIMohamed MHIRIaffiliation not provided to SSRNMohammed Hamdanaffiliation not provided to SSRNMohamed Cherietaffiliation not provided to SSRN Abstract In this work, the word spotting task is tackled in the segmentation- and the training-based scenarios. This task remains a matter of open discussion in the document analysis community because of the large intra-class and inter-class variability of handwritten shapes. In this work, three main contributions are made to solve this task. The first contribution is to investigate the Pyramid of Bidirectional Character Sequences (PBCS) representation to encode word texts. This representation allows trained models to catch character sub-sequence patterns shared across words. Consequently, words not seen during training can be also represented. The second contribution is to consider the word spotting task as a binary classification problem in the space of difference between representations. In this space, spotting out-of-vocabulary words is more efficient. The third contribution is a new deep neural network architecture that combines the strengths of convolutional layers and transformers. On the IAM and RIEMS datasets, the proposed system outperforms recent methods in both query-by-example and query-by-string scenarios. Keywords: Word spotting, query-by-example, query-by-string, PBSC representation, CNN, Vision Transformer. Suggested Citation: Suggested Citation MHIRI, Mohamed and Hamdan, Mohammed and Cheriet, Mohamed, Handwriting Word Spotting in the Space of Difference between Representations Using Vision Transformers. Available at SSRN: https://ssrn.com/abstract=4113859 Mohamed MHIRI (Contact Author) affiliation not provided to SSRN ( email ) No Address Available Mohammed Hamdan affiliation not provided to SSRN Mohamed Cheriet affiliation not provided to SSRN ( email ) No Address Available Download This Paper Open PDF in Browser Do you want regular updates from SSRN on Twitter? Follow SSRN Paper statistics Downloads 4 Abstract Views 11 PlumX Metrics Feedback Feedback to SSRN Feedback (required) Email (required) Submit If you need immediate assistance, call 877-SSRNHelp (877 777 6435) in the United States, or +1 212 448 2500 outside of the United States, 8:30AM to 6:00PM U.S. Eastern, Monday - Friday. Submit a Paper Section 508 Text Only Pages SSRN Quick Links SSRN Solutions Research Paper Series Conference Papers Partners in Publishing Jobs & Announcements Newsletter Sign Up SSRN Rankings Top Papers Top Authors Top Organizations About SSRN SSRN Objectives Network Directors Presidential Letter Announcements Contact us FAQs Copyright Terms and Conditions Privacy Policy We use cookies to help provide and enhance our service and tailor content. To learn more, visit Cookie Settings. This page was processed by aws-apollo5 in 0.297 seconds