How does content-adaptive tokenization compare to fixed-patch tokenization in terms of inference throughput (t

Abstract

Abstract: In this paper, we propose a novel approach to address the challenges of printed Urdu text recognition using high-resolution, multi-scale semantic feature extraction. Our proposed UTRNet architecture, a hybrid CNN-RNN model, demonstrates state-of-the-art performance on benchmark datasets. To address the limitations of previous works, which struggle to generalize to the intricacies of the Urdu script and the lack of sufficient annotated real-world data, we have introduced the UTRSet-Real, a large-scale annotated real-world dataset comprising over 11,000 lines and UTRSet-Synth, a synthetic datase

Research Question

How does content-adaptive tokenization compare to fixed-patch tokenization in terms of inference throughput (tokens/sec) and accuracy on the DocVQA and ChartQA benchmarks for high-resolution inputs?

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