This is ConText, a powerful generalists that could do perfect text removal and segmentation framework. We also first exploration of establishing a visual in-context learning (V-ICL) paradigm for fine-grained text recognition tasks, including text segmentation and removal. To achieve this, we sought a single-task-targeted baseline solution based on the prevailing V-ICL frameworks, which typically regulates in-context inference as a query-label-reconstruction process. Beyond simple task-specific fine-tuning, we proposed an end-to-end in-context generalist elicited from a task-chaining prompt that explicitly chaining up tasks as one enriched demonstration, leveraging inter-task correlations to improve the in-context reasoning capabilities. A Through quantitative and qualitative experiments, we demonstrated the grounding effectiveness and superiority of our framework across various in-domain and out-of-domain text recognition tasks, outperforming both current generalists and specialists. Overall, we hope this pioneering work will encourage further development of V-ICL in text recognition.

The code source is in here.

Here we provide the weights of ConText and ConTextV, you can download these checkpoints and follow the process in here to perform OCR-level removal and segmentation. Have FUN!

It reaches SOTA performance in all text segmentation and removal benchmarks.

Feel free to contact ferenas@sjtu.edu.cn if you have any problem! [More Information Needed]