Current wearable camera and computer technology opens the way for preservation of every printed, computer mediated and spoken word that an individual has ever seen or heard. Text images acquired autonomously at one frame per second by a 20 megapixel miniature camera and recorded speech, both with GPS tags, can be uploaded and stored permanently on available mobile or desktop devices. After culling redundant images and mosaicking fragments, the text can be transcribed, tagged, indexed and summarized. A combination of already developed methods of information retrieval, web science and cognitive computing will enable selective retrieval of the accumulated information. New issues are engendered by the potential advent of microcosms of personal information at a scale of about 1:1,000,000 of the World Wide Web.
Detecting overlapping text from map images is a challenging problem. Previous algorithms generally assume specific cartographic styles (e. g., road shapes and text format) and are difficult to adjust for handling different map types. In this paper, we build on our previous text recognition work, Strabo, to develop an algorithm for detecting overlapping characters from non-text symbols. We call this algorithm Overlapping Text Detection (OTD). OTD uses the recognition results and locations of detected text labels (from Strabo) to detect potential areas that contain overlapping text. Next, OTD classifies these areas as either text or non-text regions based on their shape descriptions (including the ratio of number of foreground pixels to area size, number of connected components, and number of holes). The average precision and recall of OTD in classifying text and non-text regions were 77% and 86%, respectively. We show that OTD improved the precision and recall of text detection in Strabo by 19% and 41%, respectively, and produced higher accuracy compared to a state-of- the-art text/graphic separation algorithm.