This paper describes the short-term competition on Components Segmentation Task of Document Photos that was prepared in the context of the 16th International Conference on Document Analysis and Recognition (ICDAR 2021). This competition aims to bring together researchers working on the filed of identification document image processing and provides them a suitable benchmark to compare their techniques on the component segmentation task of document images. Three challenge tasks were proposed entailing different segmentation assignments to be performed on a provided dataset. The collected data are from several types of Brazilian ID documents, whose personal information was conveniently replaced. There were 16 participants whose results obtained for some or all the three tasks show different rates for the adopted metrics, like Dice Similarity Coefficient ranging from 0.06 to 0.99. Different Deep Learning models were applied by the entrants with diverse strategies to achieve the best results in each of the tasks. Obtained results show that the current applied methods for solving one of the proposed tasks (document boundary detection) are already well stablished. However, for the other two challenge tasks (text zone and handwritten sign detection) research and development of more robust approaches are still required to achieve acceptable results.

翻译：本文介绍了在第16次文件分析和识别国际会议(ICDAR 2021)背景下编写的有关文件照片组成部分分解任务的短期竞争情况,该竞争旨在将从事身份识别文件图像处理工作的研究人员聚集在一起,并为他们提供一个适当的基准,以比较其在文件图像组成部分分解任务方面的技术;提出了三项挑战任务,需要在提供的数据集上执行不同的分解任务;所收集的数据来自几类巴西身份证文件,其个人信息被方便地取代;有16名与会者在部分或全部三项任务中取得的成果显示,采用的指标的比率不同,如Dice相似系数为0.06至0.99;为取得每项任务的最佳结果,新加入者采用了不同的深学习模式;获得的结果显示,目前用于解决一项拟议任务(文件边界探测)的方法已经相当稳定;但是,对于其他两项挑战任务(文字区和手写标志探测),还需要进行更稳健的研究和开发,以取得可接受的结果。