This limited their utility for real-life historical documents, which often contain shading, blur, shine-through, stains, skewness, complex layouts, and other things that produce OCR error. Footnote 1 Pre-trained, general OCR processors have a much higher potential for wide adoption in the scholarly community, and hence their out-of-the box performance is of scientific interest.įor long, general OCR processors such as Tesseract () only delivered perfect results under what we may call laboratory conditions, i.e., on noise-free, single-column text in a clear printed font. The best results are usually obtained with a tailored solution involving corpus-specific pre-processing, model training, or postprocessing, but such procedures can be labour-intensive.
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Automated text extraction from digital images can open up large quantities of understudied historical documents to computational analysis, potentially generating deep new insights into the human past.īut OCR is a technology still in the making, and available software provides varying levels of accuracy. The test materials have been preserved in the openly available “Noisy OCR Dataset” (NOD) for reuse in future benchmarking studies.įew technologies hold as much promise for the social sciences and humanities as optical character recognition (OCR).
Specifying the relative performance of three leading OCR products and the differential effects of commonly found noise types can help scholars identify better OCR solutions for their research needs. Accuracy for English was considerably higher than for Arabic. Document AI delivered the best results, and the server-based processors (Textract and Document AI) performed substantially better than Tesseract, especially on noisy documents. English-language book scans ( n = 322) and Arabic-language article scans ( n = 100) were replicated 43 times with different types of artificial noise for a corpus of 18,568 documents, generating 51,304 process requests.
This article reports a benchmarking experiment comparing the performance of Tesseract, Amazon Textract, and Google Document AI on images of English and Arabic text. Optical Character Recognition (OCR) can open up understudied historical documents to computational analysis, but the accuracy of OCR software varies.
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