Corpus Construction for Historical Newspapers: A Case Study on Public Meeting Corpus Construction using OCR Error Correction

Large text corpora are indispensable for natural language processing. However, in various fields such as literature and humanities, many documents to be studied are only scanned to images, but not converted to text data. Optical character recognition (OCR) is a technology to convert scanned document images into text data. However, OCR often misrecognizes characters due to the low quality of the scanned document images, which is a crucial factor that degrades the quality of constructed text corpora. This paper works on corpus construction for historical newspapers. We present a corpus construction method based on a pipeline of image processing, OCR, and filtering. To improve the quality, we further propose to integrate OCR error correction. To this end, we manually construct an OCR error correction dataset in the historical newspaper domain, propose methods to improve a neural OCR correction model and compare various OCR error correction models. We evaluate our corpus construction method on the accuracy of extracting articles of a specific topic to construct a historical newspaper corpus. As a result, our method improves the article extraction F score by 1.7% via OCR error correction comparing to previous work. This verifies the effectiveness of OCR error correction for corpus construction