A data science team is developing a pipeline to digitize a large archive of historical financial ledgers from the early 20th century. The documents are scanned in grayscale and suffer from significant issues, including page yellowing, ink bleed-through, and non-standard, multi-column layouts. An initial implementation using a standard Tesseract OCR configuration yields a very high Word Error Rate (WER). To achieve the most significant improvement in extraction accuracy, which of the following computer vision techniques should the team prioritize?
Use a super-resolution generative adversarial network (SRGAN) to increase the effective DPI of the source images before OCR processing.
Fine-tune a pre-trained Transformer-based language model on a modern financial text corpus to post-process the OCR output and correct recognition errors.
Implement adaptive thresholding for binarization, followed by document layout analysis to segment text blocks and columns before passing them to the OCR engine.
Apply data augmentation techniques such as random rotation and scaling to the training dataset of the core OCR recognition model.
The correct answer is to implement adaptive thresholding and document layout analysis. The described historical documents have two primary challenges: poor image quality (yellowing, bleed-through) and complex structure (multi-column layouts). Adaptive thresholding is a binarization technique that is highly effective for images with non-uniform illumination, calculating a different threshold for different regions of the image, which directly addresses the yellowing and bleed-through issues. Following binarization, document layout analysis is critical for segmenting the page into its constituent parts, such as columns and blocks of text, before sending them to the recognition engine. This prevents the OCR from reading text across unrelated columns and producing nonsensical output. Addressing these fundamental preprocessing and segmentation issues is the highest priority and will yield the largest improvement in accuracy.
Applying a language model for post-processing is incorrect because it is a downstream step. If the initial OCR output is too garbled due to poor image quality and incorrect layout analysis (the "Garbage In, Garbage Out" principle), a language model will have insufficient context to make meaningful corrections.
Using a super-resolution GAN (SRGAN) is incorrect because while it can improve image resolution, it does not solve the core problems of non-uniform illumination or complex layout structure. Furthermore, adaptive thresholding is a more direct and computationally less expensive solution for the contrast-related issues.
Applying data augmentation is incorrect because it is a technique used during the training of a model to improve its robustness. The scenario describes an inference task using a pre-configured tool. While a custom-trained model might eventually be needed, it is not the immediate, highest-impact step compared to fixing the preprocessing pipeline.
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