ICDAR 2003 ROBUST READING COMPETITIONS PDF

More information about each challenge is provided in their respective pages: Trial datasets serve two purposes. Four independent competitions were organised: The aim of this competition is to find the best system able to classify single characters that have been extracted from natural scenes. This page is editable only by TC11 Officers. These tasks roubst organised in a closed mode, meaning that the participants had to submit an operational version of their system for independent testing. Robust Reading is at the meeting point between camera based competitiobs analysis and scene interpretation, and serves as common ground between the document analysis community and the wider computer vision community.

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Lucas, A. Panaretos, L. Sosa, A. Tang, S. Wong and R. 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 hope to gain a better understanding of the state of the art in each of the sub-problems. Furthermore, our methodology involves 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 report the results of this contest, and show cases where the leading algorithms succeed and fail.

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ICDAR 2003 ROBUST READING COMPETITIONS PDF

Lucas, A. Panaretos, L. Sosa, A. Tang, S. Wong and R. 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.

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Introduction

Lucas, A. Panaretos, L. Sosa, A. Tang, S. Wong and R. Young Dept.

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