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Historical data sources, like medical records or biological collections, consist of unstructured heterogeneous content: handwritten text, different sizes and types of fonts, and text overlapped with lines, images, stamps, and sketches. The information these documents can provide is important, from a historical perspective and mainly because we can learn from it. The automatic digitization of these historical documents is a complex machine learning process that usually produces poor results, requiring costly interventions by experts, who have to transcribe and interpret the content. This paper describes hybrid (Human- and Machine-Intelligent) workflows for scientific data extraction, combining machine-learning and crowdsourcing software elements. Our results demonstrate that the mix of human and machine processes has advantages in data extraction time and quality, when compared to a machine-only workflow. More specifically, we show how OCRopus and Tesseract, two widely used open source Optical Character Recognition (OCR) tools, can improve their accuracy by more than 42%, when text areas are cropped by humans prior to OCR, while the total time can increase or decrease depending on the OCR selection. The digitization of 400 images, with Entomology, Bryophyte, and Lichen specimens, is evaluated following four different approaches: processing the whole specimen image (machine-only), processing crowd cropped labels (hybrid), processing crowd cropped fields (hybrid), and cleaning the machine-only output. As a secondary result, our experiments reveal differences in speed and quality between Tesseract and OCRopus.
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A Gaussian Process Upsampling Model for Improvements in Optical Character Recognition
The automatic evaluation and extraction of financial documents is a key process in business efficiency. Most of the extraction relies on the Optical Character Recognition (OCR), whose outcome is dependent on the quality of the document image. The image data fed to the automated systems can be of unreliable quality, inherently low-resolution or downsampled and compressed by a transmitting program. In this paper, we illustrate a novel Gaussian Process (GP) upsampling model for the purposes of improving OCR process and extraction through upsampling low resolution documents.
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- Award ID(s):
- 1908834
- PAR ID:
- 10280214
- Date Published:
- Journal Name:
- International Symposium on Visual Computing
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/https://doi.org/10.1007/978-3-030-64559-5_20
