Synthetic Map Generation to Provide Unlimited Training Data for Historical Map Text Detection
Synthetic Map Generation to Provide Unlimited Training Data for Historical Map Text Detection
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DOI：
10.1145/3486635.3491070
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发表时间：
2021-11
期刊：
影响因子：
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通讯作者：
Zekun Li;Runyu Guan;Qianmu Yu;Yao-Yi Chiang;Craig A. Knoblock
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作者：
Zekun Li;Runyu Guan;Qianmu Yu;Yao-Yi Chiang;Craig A. Knoblock
对于需要长期历史地理数据的研究，许多历史地图都是公开的。这些地图的制图设计包括地图符号和文本标签的组合。从地图图像中自动读取文本标签可以大大加快地图解释的速度，并有助于生成描述地图内容的丰富元数据。许多文本检测算法已经被提出来自动定位地图图像中的文本区域，但大多数算法都是在域外数据集（如风景图像）上训练的。训练数据决定了机器学习模型的质量，手动标注地图图像中的文本区域是费时费力的。另一方面，现有的地理数据源，如Open-StreetMap (OSM)，包含机器可读的地图层，这使得我们可以很容易地分离出文本层并获得文本标签注释。然而，OSM地图瓷砖和历史地图之间的制图风格有很大不同。本文提出了一种自动生成无限量带注释的历史地图图像用于训练文本检测模型的方法。我们使用风格转换模型将当代地图图像转换为历史风格，并在其上放置文本标签。我们证明了最先进的文本检测模型（例如PSENet）可以从合成历史地图中受益，并在历史地图文本检测方面取得了显着改进。
Many historical map sheets are publicly available for studies that require long-term historical geographic data. The cartographic design of these maps includes a combination of map symbols and text labels. Automatically reading text labels from map images could greatly speed up the map interpretation and helps generate rich metadata describing the map content. Many text detection algorithms have been proposed to locate text regions in map images automatically, but most of the algorithms are trained on out-of-domain datasets (e.g., scenic images). Training data determines the quality of machine learning models, and manually annotating text regions in map images is labor-extensive and time-consuming. On the other hand, existing geographic data sources, such as Open-StreetMap (OSM), contain machine-readable map layers, which allow us to separate out the text layer and obtain text label annotations easily. However, the cartographic styles between OSM map tiles and historical maps are significantly different. This paper proposes a method to automatically generate an unlimited amount of annotated historical map images for training text detection models. We use a style transfer model to convert contemporary map images into historical style and place text labels upon them. We show that the state-of-the-art text detection models (e.g., PSENet) can benefit from the synthetic historical maps and achieve significant improvement for historical map text detection.