Abstract
Abstract. Visually impaired people cannot use classical maps but can learn to use tactile relief maps. These tactile maps are crucial at school to learn geography and history as well as the other students. They are produced manually by professional transcriptors in a very long and costly process. A platform able to generate tactile maps from maps scanned from geography textbooks could be extremely useful to these transcriptors, to fasten their production. As a first step towards such a platform, this paper proposes a method to infer the scale and the content of the map from its image. We used convolutional neural networks trained with a few hundred maps from French geography textbooks, and the results show promising results to infer labels about the content of the map (e.g. ”there are roads, cities and administrative boundaries”), and to infer the extent of the map (e.g. a map of France or of Europe).
Highlights
Impaired people cannot use classical maps but can learn to use tactile relief maps, in order to understand the geography of their place of residence, hometown, region, country, or of the world
In order to provide a tactile map from a map image, several steps are necessary: (1) infer the map characteristics from the image; (2) collect the geographic data necessary to draw this map; (3) simplify and generalise this geographic data to adapt it to tactile map reading (Touya et al, 2019); (4) transform the map into a digital model able to be printed with one of the existing techniques (Lobben, 2015, Brock et al, 2015) As a first step towards this online platform, we propose in this paper a method to infer the scale and the content of the map image, based on deep learning techniques
Infering the style of the map in addition to the other infered characteristics can be useful for two reasons: (1) many visually impaired people do see some colors, so colored and visually styled maps can be useful in addition to the tactile graphics; (2) it might be possible to translate some styles into tactile counterparts, to increase the expressivity of tactile maps
Summary
Impaired people cannot use classical maps but can learn to use tactile relief maps, in order to understand the geography of their place of residence, hometown, region, country, or of the world. This is why it would be useful to apply advanced cartography techniques to accelerate tactile mapmaking (Lobben, 2015), or even to automate it (Touya et al, 2019, Wabinski and Moscicka, 2019) In this context, our long term goal is an automated online platform where geography teachers are able to send an image (e.g. scanned from a textbook) of a map, and receive the tactile map, as a file ready to be 3D printed, which is as close as possible as the original map.
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