Text segmentation in unconstrained hand-drawings in whiteboard photos
Lu, H. & Kowalkiewicz, M. (2012) Text segmentation in unconstrained hand-drawings in whiteboard photos. In Digital Image Computing Techniques and Applications (DICTA), 2012, IEEE, Freemantle, WA, pp. 1-6.
In this paper we present a robust method to detect handwritten text from unconstrained drawings on normal whiteboards. Unlike printed text on documents, free form handwritten text has no pattern in terms of size, orientation and font and it is often mixed with other drawings such as lines and shapes. Unlike handwritings on paper, handwritings on a normal whiteboard cannot be scanned so the detection has to be based on photos. Our work traces straight edges on photos of the whiteboard and builds graph representation of connected components. We use geometric properties such as edge density, graph density, aspect ratio and neighborhood similarity to differentiate handwritten text from other drawings. The experiment results show that our method achieves satisfactory precision and recall. Furthermore, the method is robust and efficient enough to be deployed in a mobile device. This is an important enabler of business applications that support whiteboard-centric visual meetings in enterprise scenarios. © 2012 IEEE.
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|Item Type:||Conference Paper|
|Keywords:||Business applications, Connected component, Edge densities, Freeforms, Geometric properties, Graph density, Graph representation, Handwritten texts, Precision and recall, Printed texts, Robust methods, Straight edge, Text segmentation, White board, Aspect ratio, Mobile devices, Information retrieval systems|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)|
|Divisions:||Current > QUT Faculties and Divisions > Science & Engineering Faculty|
|Deposited On:||29 Jul 2015 07:15|
|Last Modified:||31 Jul 2015 00:43|
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