In recent years, the optical character recognition (OCR) field has been
proliferating with plentiful cutting-edge approaches for a wide spectrum of
tasks. However, these approaches are task-specifically designed with divergent
paradigms, architectures, and training strategies, which significantly
increases the complexity of research and maintenance and hinders the fast
deployment in applications. To this end, we propose UPOCR, a
simple-yet-effective generalist model for Unified Pixel-level OCR interface.
Specifically, the UPOCR unifies the paradigm of diverse OCR tasks as
image-to-image transformation and the architecture as a vision Transformer
(ViT)-based encoder-decoder. Learnable task prompts are introduced to push the
general feature representations extracted by the encoder toward task-specific
spaces, endowing the decoder with task awareness. Moreover, the model training
is uniformly aimed at minimizing the discrepancy between the generated and
ground-truth images regardless of the inhomogeneity among tasks. Experiments
are conducted on three pixel-level OCR tasks including text removal, text
segmentation, and tampered text detection. Without bells and whistles, the
experimental results showcase that the proposed method can simultaneously
achieve state-of-the-art performance on three tasks with a unified single
model, which provides valuable strategies and insights for future research on
generalist OCR models. Code will be publicly available.

提出了一种名为 UPOCR 的简单而有效的通用模型，用于统一的像素级光学字符识别接口，通过图像转化和基于视觉 Transformer 的编码器 - 解码器结构统一了多样的 OCR 任务的范式，并引入可学习的任务提示使解码器具有任务感知性，在三个像素级 OCR 任务上的实验结果显示，该方法可以在一个统一的模型上同时实现三个任务的最先进性能，为未来关于通用 OCR 模型的研究提供了有价值的策略和见解。