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Baseline Experiments for OCR-Based Arabic Named-Entity Extraction

The named-entity extraction task is concerned with extracting higher-level information chunks such as names of people, organization, time, and dates from messages. Most recognition systems that perform this task assume that the input to the system is noise-free symbolic text. In this paper we explore the impact of OCR error on the accuracy of named-entity recognition systems. We report results of our baseline experiments that we conducted using an off-the-shelf named-entity recognizer and a commercial OCR product. We hope that the dataset and the experimental results will stimulate cross-disciplinary research and allow researchers to measure progress each year.