MC-OCR挑战2021:面向无约束移动捕获越南收据的文档理解

Hoai Viet Nguyen, Linh Doan Bao, Hoang Viet Trinh, Hoang Huy Phan, Ta Minh Thanh
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引用次数: 2

摘要

移动捕获收据光学字符识别(MC-OCR)[14]挑战包含两个任务:收据图像质量评估和关键信息提取。在第一个任务中,我们引入了一个回归模型来映射各种输入,例如输出OCR的概率,裁剪的文本框,图像到实际标签。在第二项任务中,我们提出了一个堆叠多模型来解决这个问题。鲁棒模型包括图像分割、图像分类、文本检测、文本识别和文本分类。按照这个解决方案,我们可以得到重要的解决各种噪音收据类型:水平,倾斜和模糊收据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MC-OCR Challenge 2021: Towards Document Understanding for Unconstrained Mobile-Captured Vietnamese Receipts
The Mobile capture receipts Optical Character Recognition (MC-OCR) [14] challenge deliver two tasks: Receipt Image Quality Evaluation and Key Information Extraction. In the first task, we introduce a regression model to map various inputs, for instance the probability of the output OCR, cropped text boxes, images to actual label. In the second task, we propose a stacked multi-model as a solution to solve this problem. The robust models are incorporated by image segmentation, image classification, text detection, text recognition, and text classification. Follow this solution, we can get vital tackle various noise receipt types: horizontal, skew, and blur receipt.
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