论文标题
有限且嘈杂的数据，可靠的手写识别
Robust Handwriting Recognition with Limited and Noisy Data
论文作者
论文摘要
尽管在计算机视觉中进行了深入学习的出现，但一般的手写识别问题尚未解决。大多数现有的方法都集中在手写数据集上，这些数据集清楚地写了文本和精心段的标签。在本文中，我们专注于从维护日志中学习手写字符，这是一个受约束的设置，其中数据非常有限且嘈杂。我们分别将问题分别分为两个连续的单词分割和单词识别阶段，并利用数据增强技术来训练这两个阶段。与流行的基线进行场景文本检测和单词识别的广泛比较表明，我们的系统达到了较低的错误率，并且更适合处理嘈杂和困难的文档
Despite the advent of deep learning in computer vision, the general handwriting recognition problem is far from solved. Most existing approaches focus on handwriting datasets that have clearly written text and carefully segmented labels. In this paper, we instead focus on learning handwritten characters from maintenance logs, a constrained setting where data is very limited and noisy. We break the problem into two consecutive stages of word segmentation and word recognition respectively and utilize data augmentation techniques to train both stages. Extensive comparisons with popular baselines for scene-text detection and word recognition show that our system achieves a lower error rate and is more suited to handle noisy and difficult documents
