Automatic Text Removal and Replacement in Scanned Meteorological Thematic Maps

Abstract

Geo-referenced data is required in many Geographic Information System (GIS) applications and one source of such data being maps. Meteorological maps are color-coded with different regions corresponding to different values of a parameter, parsing the image to convert into data is not very difficult. However, text and different planimetric elements overlaid on the regions in the map makes accurate image to data conversion a challenging problem, because it is almost impossible to exactly replace what was underneath the text or icons i.e., the need for inpainting. In this study, we propose a probabilistic technique that uses the probability of occurrence of colors present in the map along with the occurrence of those colors in the spatial neighborhood of the pixel (corresponding to text) whose color is required to be replaced. We test the limits of our proposed technique using simulated data and compare results with a popular image editing tool using public domain data with promising results.

Automated Extraction of Labels from Large-Scale Historical Maps

“…2). This is assumed to be a main drawback in the course of text detection (Abdullah et al, 2015;Tofani and Kasturi, 1998). A vast amount of existing algorithms operate on the assumption that black text is in contrast to different-colored features.…”

“…• Low graphical quality (Abdullah et al, 2015;Yu et al, 2017;Chiang et al, 2016 as shown in Fig. 1 1081 x 881 41% → 58% → 66% 100% → 91% → 91% 58% → 71% → 77% subset of Fig.…”

An Integrated Framework for Predicting Long-Term Productivity of Pastures in the Kingdom of Saudi Arabia

“…and contains a great deal of 'noise' making its optimum use difficult. Therefore, image inpainting techniques [24,25] are used (which is part of the framework [26]) to remove 'noise' (such as city names, administrative boundaries, roads and other symbols/icons) from the available maps in digital or hard copy format. Once these maps are cleaned and the 'noise' is removed and replaced with the correct data, using our framework the raster maps are converted into comma separated values (CSVs) and then loaded into the spatial data warehouse (SDW) as georeferenced tables.…”