Abstract
Writing and drawing are basic forms of human communication. Handwritten and hand-drawn documents are often used at initial stages of a project. For storage and later usage, handwritten documents are often converted into a digital format with a graphics program. Drawing with a computer in many cases requires skill and more time than less formal handwritten drawings. Even when people have experience in computer drawing and are familiar with the application, it takes time. Automatic conversion of images of hand-drawn diagrams into a digital graphic format file could save time in the design process. One of early critical tasks in hand-drawn diagram interpretation is segmentation of the diagram into text and non-text components. In this paper, we compare two approaches for offline text and non-text segmentation of contours in an image. We describe the feature extraction and classification processes. Our methods obtain 82–86 % accuracy. Future work will explore the application of these techniques in a complete diagram interpretation system.
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Pravalpruk, B., Dailey, M.M. (2016). Offline Text and Non-text Segmentation for Hand-Drawn Diagrams. In: Booth, R., Zhang, ML. (eds) PRICAI 2016: Trends in Artificial Intelligence. PRICAI 2016. Lecture Notes in Computer Science(), vol 9810. Springer, Cham. https://doi.org/10.1007/978-3-319-42911-3_32
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DOI: https://doi.org/10.1007/978-3-319-42911-3_32
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