Introduction

In the recent years most libraries around the world have initiated projects for the complete digitization of their map collections so as to enable easy access to information. When it comes to paper documents, optical character recognition (OCR) techniques are widely used for text digitization. Although maps contain a wealth of alphanumeric information, recent literature has indicated very limited examples of OCR implementation on maps. These examples are mainly focused on the extraction of textual information, whereas the extraction of numerical information has not been examined separately. Purpose of this research is to evaluate how an OCR algorithm can be used to extract numerical information from maps in form of bathymetric points and house numbers, which can ultimately lead in the automatic generation of geographic information layers. Additionally, the possibility of an automated georeference method is examined based on the recognition of coordinates on the map borders and their assignment to the corresponding grid intersections.

Methodology

To evaluate the proposed method an OCR algorithm was developed in Python language, using the OpenCV library. The overall process can be separated into 3 main stages (Fig.1). The first is the pre-processing of the image, which includes the conversion to greyscale and the binarization of the image. Additional filters of Gaussian blur and Erosion/Dilation are used in cases of complicated background or noise (Fig.2). The feature extraction stage involves the digit/graphics separation, the segmentation of individual digits and the extraction of a feature set that can describe each of them (Fig.3). In this research the intensity values of the pixels of each segmented images were used as descriptors. The last stage of the OCR algorithm is the classification, where each segmented image is classified as one of the ten possible digits. The adjacent digits that form one multi-digit number are grouped together and then assigned in the correct position on the map (Fig.4). Regarding the proposed method of georeference, Hough transform is used for the detection of the grid lines and then the intersections, which will serve as control points, are identified (Fig.6). OCR is applied then on the close area of each intersection and the recognised coordinates are being assigned to the intersection (Fig.5).

Results and further improvements

The produced algorithm achieves a very good recognition rate and most digits - in form of bathymetric points - were succesfully recognised (Fig.4). However, the overall accuracy of the algorithm is highly dependent on the characteristics of each map and the complexity of the graphics. Thus, customisation is necessary for application on different map series. Regarding the georeference, the complete automation of the process is not an easy task, as each individual map has to be handled differently, requiring the user’s intervention in multiple steps throughout the process. (Fig.5). Undoubtedly, the algorithm could be improved to tackle some of the problems that are encountered throughout the whole process. For example, the multi-oriented digits can be succesfully reognised using rotation inveriant descriptors. Additionally, the developed OCR algorithm could be enriched to support the recognition of latin alphabet characters. This would enable the recognition of toponyms, which would in turn facilitate the georeference process by locating roughly the position and scale of the map with an alignment to a gazeteer of known place names.

Recommented Literature

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