In recent years, end-to-end scene text spotting approaches are evolving to the Transformer-
based framework. While previous studies have shown the crucial importance of the intrinsic …

With the world population rapidly increasing, transforming our agrifood systems to be more
productive, efficient, safe, and sustainable is crucial to mitigate potential food shortages …

This paper presents a comprehensive evaluation of the Optical Character Recognition
(OCR) capabilities of the recently released GPT-4V (ision), a Large Multimodal Model …

Despite CLIP being the foundation model in numerous vision-language applications, CLIP
suffers from a severe text spotting bias. Such bias causes CLIP models to 'Parrot'the visual …

Recently visually-situated text parsing (VsTP) has experienced notable advancements
driven by the increasing demand for automated document understanding and the …

We exploit the potential of the large-scale Contrastive Language-Image Pretraining (CLIP)
model to enhance scene text detection and spotting tasks, transforming it into a robust …

More and more end-to-end text spotting methods based on Transformer architecture have
demonstrated superior performance. These methods utilize a bipartite graph matching …

Reading text from images (either natural scenes or documents) has been a long-standing
research topic for decades, due to the high technical challenge and wide application range …

Text on historical maps contains valuable information providing georeferenced historical,
political, and cultural contexts. However, text extraction from historical maps has been …

Modularity plays a crucial role in the development and maintenance of complex systems.
While end-to-end text spotting efficiently mitigates the issues of error accumulation and sub …