Spatially prioritized and persistent text detection and decoding

Hsueh-Cheng Wang*, Yafim Landa, Maurice Fallon, Seth Teller

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations


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.

Original languageEnglish
Title of host publicationCamera-Based Document Analysis and Recognition - 5th International Workshop, CBDAR 2013, Revised Selected Papers
PublisherSpringer Verlag
Number of pages15
ISBN (Print)9783319051666
StatePublished - 2014
Event5th International Workshop on Camera-Based Document Analysis and Recognition, CBDAR 2013 - Washington, DC, United States
Duration: 23 Aug 201323 Aug 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8357 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference5th International Workshop on Camera-Based Document Analysis and Recognition, CBDAR 2013
Country/TerritoryUnited States
CityWashington, DC


  • DCT
  • Language model
  • Lexicon
  • MSER
  • Multiple frame integration
  • SLAM
  • Text detection
  • Video OCR


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