This paper introduces DONUT-hole, a sparse OCR-free visual document understanding (VDU) model that addresses the limitations of its predecessor model, dubbed DONUT. The DONUT model, leveraging a transformer architecture, overcoming the challenges of separate optical character recognition (OCR) and visual semantic understanding (VSU) components. However, its deployment in production environments and edge devices is hindered by high memory and computational demands, particularly in large-scale request services. To overcome these challenges, we propose an optimization strategy based on knowledge distillation and model pruning. Our paradigm to produce DONUT-hole, reduces the model denisty by 54\% while preserving performance. We also achieve a global representational similarity index between DONUT and DONUT-hole based on centered kernel alignment (CKA) metric of 0.79. Moreover, we evaluate the effectiveness of DONUT-hole in the document image key information extraction (KIE) task, highlighting its potential for developing more efficient VDU systems for logistic companies.

翻译：本文提出DONUT-hole，一种稀疏无OCR视觉文档理解（VDU）模型，旨在解决其前身模型DONUT的局限性。DONUT模型基于Transformer架构，克服了独立光学字符识别（OCR）与视觉语义理解（VSU）组件的挑战。然而，由于对内存和计算资源的高需求，特别是在大规模请求服务中，该模型在生产环境与边缘设备上的部署受到阻碍。为应对这些挑战，我们提出一种基于知识蒸馏与模型剪枝的优化策略。通过该范式生成的DONUT-hole，在保持性能的同时将模型密度降低54%。我们基于中心核对齐（CKA）指标，实现了DONUT与DONUT-hole之间0.79的全局表征相似性指数。此外，我们在文档图像关键信息提取（KIE）任务中评估了DONUT-hole的有效性，突显了其为物流公司开发更高效VDU系统的潜力。