Abstract
We show how to exploit temporal and spatial coherence to achieve efficient and effective text detection and decoding for a sensor suite moving through an environment in which text occurs at a variety of locations, scales and orientations with respect to the observer. Our method uses simultaneous localization and mapping (SLAM) to extract planar “tiles” representing scene surfaces. Multiple observations of each tile, captured from different observer poses, are aligned using homography transformations. Text is detected using Discrete Cosine Transform (DCT) and Maximally Stable Extremal Regions (MSER), and decoded by an Optical Character Recognition (OCR) engine. The decoded characters are then clustered into character blocks to obtain an MLE word configuration. This paper’s contributions include: (1) spatiotemporal fusion of tile observations via SLAM, prior to inspection, thereby improving the quality of the input data; and (2) combination of multiple noisy text observations into a single higher-confidence estimate of environmental text.
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Acknowledgment
We thank the Andrea Bocelli Foundation for their support, and Javier Velez and Ben Mattinson for their contributions.
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Wang, HC., Landa, Y., Fallon, M., Teller, S. (2014). Spatially Prioritized and Persistent Text Detection and Decoding. In: Iwamura, M., Shafait, F. (eds) Camera-Based Document Analysis and Recognition. CBDAR 2013. Lecture Notes in Computer Science(), vol 8357. Springer, Cham. https://doi.org/10.1007/978-3-319-05167-3_1
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DOI: https://doi.org/10.1007/978-3-319-05167-3_1
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