Text Detection and Recognition for Semantic Mapping in Indoor Navigation

Sami, Muhammad; Ayaz, Yasar; Jamil, Mohsin; Gilani, Syed Omer; Naveed, Muhammad

doi:10.1109/icitcs.2015.7293024

Cited by 2 publications

(1 citation statement)

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“…Most text recognition systems implemented optical character recognition (OCR) for classification [86][87][88], which is an off-the-shelf technology to convert images of typed, handwritten or printed text into machine-encoded text. Sami et al [87] adopted a back-projection of the colour histogram to locate interest regions and applied the Canny edge detector to remove background. A pan/tilt/zoom camera was used in [88] to provide better focusing performance on potential text regions in the wild.…”

Section: Hybrid Approachesmentioning

confidence: 99%

Extracting Semantic Information from Visual Data: A Survey

Liu

et al. 2016

Robotics

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Abstract:The traditional environment maps built by mobile robots include both metric ones and topological ones. These maps are navigation-oriented and not adequate for service robots to interact with or serve human users who normally rely on the conceptual knowledge or semantic contents of the environment. Therefore, the construction of semantic maps becomes necessary for building an effective human-robot interface for service robots. This paper reviews recent research and development in the field of visual-based semantic mapping. The main focus is placed on how to extract semantic information from visual data in terms of feature extraction, object/place recognition and semantic representation methods.

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Section: Hybrid Approachesmentioning

confidence: 99%