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Author (up) Adarsh Tiwari; Sanket Biswas; Josep Llados
Title Can Pre-trained Language Models Help in Understanding Handwritten Symbols? Type Conference Article
Year 2023 Publication 17th International Conference on Document Analysis and Recognition Abbreviated Journal
Volume 14193 Issue Pages 199–211
Abstract The emergence of transformer models like BERT, GPT-2, GPT-3, RoBERTa, T5 for natural language understanding tasks has opened the floodgates towards solving a wide array of machine learning tasks in other modalities like images, audio, music, sketches and so on. These language models are domain-agnostic and as a result could be applied to 1-D sequences of any kind. However, the key challenge lies in bridging the modality gap so that they could generate strong features beneficial for out-of-domain tasks. This work focuses on leveraging the power of such pre-trained language models and discusses the challenges in predicting challenging handwritten symbols and alphabets.
Address San Jose; CA; USA; August 2023
Corporate Author Thesis
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Language Summary Language Original Title
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Area Expedition Conference ICDAR
Notes DAG Approved no
Call Number Admin @ si @ TBL2023 Serial 3908
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