ICDAR 2003 Robust Reading Competitions: Entries, Results and Future Directions

Abstract : This paper describes the robust reading competitions for ICDAR 2003. With the rapid growth in research over the last few years on recognizing text in natural scenes, there is an urgent need to establish some common benchmark datasets, and gain a clear understanding of the current state of the art. We use the term robust reading to refer to text images that are beyond the capabilities of current commercial OCR packages. We chose to break down the robust reading problem into three sub-problems, and run competitions for each stage, and also a competition for the best overall system. The sub-problems we chose were text locating, character recognition and word recognition. By breaking down the problem in this way, we hoped to gain a better understanding of the state of the art in each of the sub-problems. Furthermore, our methodology involved storing detailed results of applying each algorithm to each image in the data sets, allowing researchers to study in depth the strengths and weaknesses of each algorithm. The text locating contest was the only one to have any entries. We give a brief description of each entry, and present the results of this contest, showing cases where the leading entries succeed and fail. We also describe an algorithm for combining the outputs of the individual text locaters, and show how the combination scheme improves on any of the individual systems.
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Submitted on : Wednesday, May 24, 2017 - 1:47:55 PM
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  • HAL Id : hal-01527429, version 1


S Lucas, Alex Panaretos, Luis Sosa, Anthony Tang, Shirley Wong, et al.. ICDAR 2003 Robust Reading Competitions: Entries, Results and Future Directions. International Journal of Document Analysis and Recognition, 2005, 2-3, 7, pp.105-122. ⟨hal-01527429⟩



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