Post-OCR Document Correction with Large Ensembles of Character Sequence-to-Sequence Models
Juan Antonio Ramirez-Orta, Eduardo Xamena, Ana MaguitmanCONICET/UNS))Dalhousie University)Universidad Nacional del Sur)
[AAAI-22] Main Track
Abstract:
In this paper, we propose a novel method to extend sequence-to-sequence models to accurately process sequences much longer than the ones used during training while being sample- and resource-efficient, supported by thorough experimentation. To investigate the effectiveness of our method, we apply it to the task of correcting documents already processed with Optical Character Recognition (OCR) systems using sequence-to-sequence models based on characters. We test our method on nine languages of the ICDAR 2019 competition on post-OCR text correction and achieve a new state-of-the-art performance in five of them. The strategy with the best performance involves splitting the input document in character n-grams and combining their individual corrections into the final output using a voting scheme that is equivalent to an ensemble of a large number of sequence models. We further investigate how to weigh the contributions from each one of the members of this ensemble. Our code for post-OCR correction is shared at https://github.com/jarobyte91/post_ocr_correction.
Introduction Video
Sessions where this paper appears
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Poster Session 5
Red 5
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Poster Session 12
Red 5
