Abstract
Post-processing is a crucial step in improving the performance of OCR process. In this paper, we present a novel approach which explores a modified way of candidate generating and candidate scoring at character level as well as word level. These features are combined with some important features suggested by related work for ranking candidates in a regression model. The experimental results show that our approach has comparable results with the top performing approaches in the Post-OCR text correction competition ICDAR 2017.
This work has been supported by the European Union’s Horizon 2020 research and innovation programme under grant 770299 (NewsEye).
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https://sites.google.com/view/icdar2017-postcorrectionocr/, last visited on 28 June 2018.
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Nguyen, TTH., Coustaty, M., Doucet, A., Jatowt, A., Nguyen, NV. (2018). Adaptive Edit-Distance and Regression Approach for Post-OCR Text Correction. In: Dobreva, M., Hinze, A., Žumer, M. (eds) Maturity and Innovation in Digital Libraries. ICADL 2018. Lecture Notes in Computer Science(), vol 11279. Springer, Cham. https://doi.org/10.1007/978-3-030-04257-8_29
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