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| Author | 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 | |
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| 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 | ||||
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| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ TBL2023 | Serial | 3908 | ||
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