PT Unknown
AU Adarsh Tiwari
   Sanket Biswas
   Josep Llados
TI Can Pre-trained Language Models Help in Understanding Handwritten Symbols?
BT 17th International Conference on Document Analysis and Recognition
PY 2023
BP 199–211
VL 14193
AB 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.
ER