Titlebook: Document Analysis and Recognition - ICDAR 2023; 17th International C Gernot A. Fink,Rajiv Jain,Richard Zanibbi Conference proceedings 2023

窃喜

山羊

和平

E2TIMT: Efficient and Effective Modal Adapter for Text Image Machine Translation, both two-stage cascade and one-stage end-to-end architectures, suffer from different issues. The cascade models can benefit from the large-scale optical character recognition (OCR) and MT datasets but the two-stage architecture is redundant. The end-to-end models are efficient but suffer from trai

物种起源

Open-Set Text Recognition via Shape-Awareness Visual Reconstructionmpared to conventional counterparts, the OSTR task demands actively spotting and incrementally recognizing novel characters. Existing methods have demonstrated some success, yet confusion among similar characters remains to be a major challenge, potentially due to insufficient shape information pres

爱国者

Accelerating Transformer-Based Scene Text Detection and Recognition via Token Pruningnd all current state-of-the-art models and have achieved excellent performance. However, the computational requirements of the transformer architecture makes training these methods slow and resource heavy. In this paper, we introduce a new token pruning strategy that significantly decreases training

engender

Text Enhancement: Scene Text Recognition in Hazy Weatherer adverse weather conditions with poor visibility remains challenging. To address this problem, we propose a text image enhancement network that can be embedded into a scene text recognizer in a pluggable manner. This network comprises multiple sets of digital image processing (DIP) units, which ar

parsimony

Reading Between the Lanes: Text VideoQA on the Roadtion is a challenging problem, while textual cues typically appear for a short time span, and early detection at a distance is necessary. Systems that exploit such information to assist the driver should not only extract and incorporate visual and textual cues from the video stream but also reason o