HOTSPOT brectly completes the sentence.

Optical Character Recognition (OCR) is the specific technology used to convert images of typed, handwritten, or printed text—such as a scanned newspaper article—into machine-readable digital text. This process allows the historian to 'digitize' the articles, making them searchable, editable, and analyzable as text data.

The other options are incorrect:

• Facial analysis is used to detect and analyze human faces, which is irrelevant to digitizing text.
• Image classification would assign a single label to the entire image (e.g., "newspaper" or "article"), not extract its content.
• Object detection would draw bounding boxes around elements like headlines or photos but would not transcribe the words.

Mori, S., Suen, C. Y., & Yamamoto, K. (1992). Historical review of OCR research and development. Proceedings of the IEEE, 80(7), 1029-1058.

DOI: https://doi.org/10.1109/5.156468

Reference (Section I, Introduction): The paper defines the fundamental goal of OCR as the "problem of converting handwritten or machine-printed text into machine-readable text." This directly aligns with the historian's goal.

Holley, R. (2009). How Good Can It Get? Analysing and Improving OCR Accuracy in Large Scale Historic Newspaper Digitisation Programs. D-Lib Magazine, 15(3/4).

DOI: https://doi.org/10.1045/march2009-holley

Reference (Abstract): This article explicitly discusses the use of "Optical Character Recognition (OCR)" as the core technology for "historic newspaper digitisation programs," which is the exact scenario in the question.

Smith, R. (2007). An Overview of the Tesseract OCR Engine. Proceedings of the Ninth International Conference on Document Analysis and Recognition (ICDAR 2007), Vol. 2, pp. 629-633.

DOI: https://doi.org/10.1109/ICDAR.2007.4376991

Reference (Section II.A, Architecture): "The Tesseract OCR engine... takes a binary image as input... and produces text output." This describes the technical process of converting an image (the scanned newspaper) into text (the digitized version).

Zou, Z., Shi, Z., Guo, Y., & Ye, J. (2019). Object Detection in 20 Years: A Survey. arXiv preprint arXiv:1905.05055.

DOI: https://doi.org/10.48550/arXiv.1905.05055

Reference (Section 1, Introduction): This paper defines object detection as a task to "localize and classify" objects. It distinguishes this from image classification (labeling the whole image) and segmentation. Neither task involves extracting text.

Zhao, W., Chellappa, R., Phillips, P. J., & Rosenfeld, A. (2003). Face recognition: A literature survey. ACM computing surveys (CSUR), 35(4), 399-458.

DOI: https://doi.org/10.1145/954339.954342

Reference (Section 1, Introduction): This survey defines face recognition (a component of facial analysis) as an application for "automatically identifying or verifying a person from a digital image." This clearly differentiates it from the text-based task.