Fewer Working Pixels, Lower Foreground F1: A Measured Document-Processing Tradeoff

Authors

  • Gregory Franklin Author
  • Ethan Hughes Author
  • Paul Lewis Author

Abstract

Reducing a document's working grid saved processing time while losing foreground accuracy in this experiment. Ten H-DIBCO 2018 pages were processed at native resolution and at approximately half each dimension, with a correspondingly reduced local threshold window. The smaller pipeline used about one quarter of the working pixels and had a geometric mean latency ratio of 0.300, while mean foreground F1 fell from 0.648 to 0.629. Timing includes reduction and binary expansion back to the original grid. The result describes a specific digital processing policy on one CPU, rather than a general speed or quality guarantee.

Exploratory research draft. One public document cohort and one CPU timing session; final scientific, originality, and document review remains pending.

Original publication: October 28, 2026.

References

  1. Talebi, Hossein and Peyman Milanfar. "NIMA: Neural Image Assessment" arXiv preprint arXiv:1709.05424, 2017. https://arxiv.org/abs/1709.05424.
  2. Kalibera, Tomas; Jones, Richard. (2013). Rigorous benchmarking in reasonable time. Proceedings of the 2013 international symposium on memory management. https://doi.org/10.1145/2464157.2464160.
  3. Varma, Sudhir and Richard Simon. “Bias in error estimation when using cross-validation for model selection.” BMC Bioinformatics, vol. 7, no. 1, 2006. https://doi.org/10.1186/1471-2105-7-91.
  4. Ke, Junjie, et al. "MUSIQ: Multi-scale Image Quality Transformer" arXiv preprint arXiv:2108.05997, 2021. https://arxiv.org/abs/2108.05997.
  5. Lin Zhang, ; Lei Zhang, ; Xuanqin Mou, ; Zhang, D.. (2011). FSIM: A Feature Similarity Index for Image Quality Assessment. IEEE Transactions on Image Processing. https://doi.org/10.1109/tip.2011.2109730.
  6. Williams, Samuel; Waterman, Andrew; Patterson, David. (2009). Roofline: an insightful visual performance model for multicore architectures. Communications of the ACM. https://doi.org/10.1145/1498765.1498785.
  7. Goldberg, David. (1991). What every computer scientist should know about floating-point arithmetic. ACM Computing Surveys. https://doi.org/10.1145/103162.103163.
  8. Mytkowicz, Todd; Diwan, Amer; Hauswirth, Matthias; Sweeney, Peter F.. (2009). Producing wrong data without doing anything obviously wrong!. Proceedings of the 14th international conference on Architectural support for programming languages and operating systems. https://doi.org/10.1145/1508244.1508275.
  9. Sauvola, J.; Pietikäinen, M.. (2000). Adaptive document image binarization. Pattern Recognition. https://doi.org/10.1016/s0031-3203(99)00055-2.
  10. White, Halbert. “A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity.” Econometrica, vol. 48, no. 4, 1980, pp. 817-838. https://doi.org/10.2307/1912934.
  11. Gebru, Timnit, et al. "Datasheets for datasets" Communications of the ACM, 2021. https://doi.org/10.1145/3458723.
  12. Roberts, David R., et al. “Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure.” Ecography, vol. 40, no. 8, 2017, pp. 913-929. https://doi.org/10.1111/ecog.02881.
  13. Brooks, Tim; Mildenhall, Ben; Xue, Tianfan; Chen, Jiawen; Sharlet, Dillon; Barron, Jonathan T.. (2018). Unprocessing Images for Learned Raw Denoising. https://arxiv.org/abs/1811.11127.
  14. Cameron, A. Colin, and Douglas L. Miller. “A Practitioner’s Guide to Cluster-Robust Inference.” Journal of Human Resources, vol. 50, no. 2, 2015, pp. 317-372. https://doi.org/10.3368/jhr.50.2.317.
  15. Shannon, C.E.. (1949). Communication in the Presence of Noise. Proceedings of the IRE. https://doi.org/10.1109/jrproc.1949.232969.
  16. Yang, Sidi, et al. "MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment." arXiv preprint arXiv:2204.08958, 2022. https://arxiv.org/abs/2204.08958.
  17. Higham, Nicholas J.. (2002). Accuracy and Stability of Numerical Algorithms: Second Edition. Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9780898718027.
  18. Cook, R. Dennis. (1977). Detection of Influential Observation in Linear Regression. Technometrics. https://doi.org/10.1080/00401706.1977.10489493.
  19. Harris, Charles R.; Millman, K. Jarrod; van der Walt, Stéfan J.; Gommers, Ralf; Virtanen, Pauli; Cournapeau, David; Wieser, Eric; Taylor, Julian; Berg, Sebastian; Smith, Nathaniel J.; Kern, Robert; Picus, Matti; Hoyer, Stephan; van Kerkwijk, Marten H.; Brett, Matthew; Haldane, Allan; del Río, Jaime Fernández; Wiebe, Mark; Peterson, Pearu; Gérard-Marchant, Pierre; Sheppard, Kevin; Reddy, Tyler; Weckesser, Warren; Abbasi, Hameer; Gohlke, Christoph; Oliphant, Travis E.. (2020). Array programming with NumPy. Nature. https://doi.org/10.1038/s41586-020-2649-2.
  20. Meng, Zijie; Zeng, Yuanze; Chang, Xiang; Xu, Tianshuo; Chao, Fei; Cao, Xixin; Shang, Changjing; Shen, Qiang. (2025). Orpaint: a zero-shot inpainting model for oracle bone inscription rubbings with visual mamba block. Science China Information Sciences, 68(8), 189102. https://doi.org/10.1007/s11432-024-4493-4.
  21. Wilkinson, Mark D.; Dumontier, Michel; Aalbersberg, IJsbrand Jan; Appleton, Gabrielle; Axton, Myles; Baak, Arie; Blomberg, Niklas; Boiten, Jan-Willem; da Silva Santos, Luiz Bonino; Bourne, Philip E.; Bouwman, Jildau; Brookes, Anthony J.; Clark, Tim; Crosas, Mercè; Dillo, Ingrid; Dumon, Olivier; Edmunds, Scott; Evelo, Chris T.; Finkers, Richard; Gonzalez-Beltran, Alejandra; Gray, Alasdair J.G.; Groth, Paul; Goble, Carole; Grethe, Jeffrey S.; Heringa, Jaap; ’t Hoen, Peter A.C; Hooft, Rob; Kuhn, Tobias; Kok, Ruben; Kok, Joost; Lusher, Scott J.; Martone, Maryann E.; Mons, Albert; Packer, Abel L.; Persson, Bengt; Rocca-Serra, Philippe; Roos, Marco; van Schaik, Rene; Sansone, Susanna-Assunta; Schultes, Erik; Sengstag, Thierry; Slater, Ted; Strawn, George; Swertz, Morris A.; Thompson, Mark; van der Lei, Johan; van Mulligen, Erik; Velterop, Jan; Waagmeester, Andra; Wittenburg, Peter; Wolstencroft, Katherine; Zhao, Jun; Mons, Barend. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data. https://doi.org/10.1038/sdata.2016.18.
  22. Wang, Zhou, Alan C. Bovik, Hamid R. Sheikh, and Eero P. Simoncelli. "Image Quality Assessment: From Error Visibility to Structural Similarity." IEEE Transactions on Image Processing, vol. 13, no. 4, 2004, pp. 600-612. https://doi.org/10.1109/TIP.2003.819861.
  23. Mittal, A.; Moorthy, A. K.; Bovik, A. C.. (2012). No-Reference Image Quality Assessment in the Spatial Domain. IEEE Transactions on Image Processing. https://doi.org/10.1109/tip.2012.2214050.
  24. Ding, Keyan, et al. "Image Quality Assessment: Unifying Structure and Texture Similarity." arXiv preprint arXiv:2004.07728, 2020. https://arxiv.org/abs/2004.07728.
  25. Zhang, Richard, et al. "The Unreasonable Effectiveness of Deep Features as a Perceptual Metric" arXiv preprint arXiv:1801.03924, 2018. https://arxiv.org/abs/1801.03924.
  26. Pratikakis, Ioannis; Zagori, Konstantinos; Kaddas, Panagiotis; Gatos, Basilis. (2018). ICFHR 2018 Competition on Handwritten Document Image Binarization (H-DIBCO 2018). 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR). https://doi.org/10.1109/icfhr-2018.2018.00091.
  27. Otsu, Nobuyuki. (1979). A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics. https://doi.org/10.1109/tsmc.1979.4310076.
  28. Chen, Chen; Chen, Qifeng; Xu, Jia; Koltun, Vladlen. (2018). Learning to See in the Dark. https://arxiv.org/abs/1805.01934.
  29. Virtanen, Pauli; Gommers, Ralf; Oliphant, Travis E.; Haberland, Matt; Reddy, Tyler; Cournapeau, David; Burovski, Evgeni; Peterson, Pearu; Weckesser, Warren; Bright, Jonathan; van der Walt, Stéfan J.; Brett, Matthew; Wilson, Joshua; Millman, K. Jarrod; Mayorov, Nikolay; Nelson, Andrew R. J.; Jones, Eric; Kern, Robert; Larson, Eric; Carey, C J; Polat, İlhan; Feng, Yu; Moore, Eric W.; VanderPlas, Jake; Laxalde, Denis; Perktold, Josef; Cimrman, Robert; Henriksen, Ian; Quintero, E. A.; Harris, Charles R.; Archibald, Anne M.; Ribeiro, Antônio H.; Pedregosa, Fabian; van Mulbregt, Paul; SciPy 1.0 Contributors; Vijaykumar, Aditya; Bardelli, Alessandro Pietro; Rothberg, Alex; Hilboll, Andreas; Kloeckner, Andreas; Scopatz, Anthony; Lee, Antony; Rokem, Ariel; Woods, C. Nathan; Fulton, Chad; Masson, Charles; Häggström, Christian; Fitzgerald, Clark; Nicholson, David A.; Hagen, David R.; Pasechnik, Dmitrii V.; Olivetti, Emanuele; Martin, Eric; Wieser, Eric; Silva, Fabrice; Lenders, Felix; Wilhelm, Florian; Young, G.; Price, Gavin A.; Ingold, Gert-Ludwig; Allen, Gregory E.; Lee, Gregory R.; Audren, Hervé; Probst, Irvin; Dietrich, Jörg P.; Silterra, Jacob; Webber, James T; Slavič, Janko; Nothman, Joel; Buchner, Johannes; Kulick, Johannes; Schönberger, Johannes L.; de Miranda Cardoso, José Vinícius; Reimer, Joscha; Harrington, Joseph; Rodríguez, Juan Luis Cano; Nunez-Iglesias, Juan; Kuczynski, Justin; Tritz, Kevin; Thoma, Martin; Newville, Matthew; Kümmerer, Matthias; Bolingbroke, Maximilian; Tartre, Michael; Pak, Mikhail; Smith, Nathaniel J.; Nowaczyk, Nikolai; Shebanov, Nikolay; Pavlyk, Oleksandr; Brodtkorb, Per A.; Lee, Perry; McGibbon, Robert T.; Feldbauer, Roman; Lewis, Sam; Tygier, Sam; Sievert, Scott; Vigna, Sebastiano; Peterson, Stefan; More, Surhud; Pudlik, Tadeusz; Oshima, Takuya; Pingel, Thomas J.; Robitaille, Thomas P.; Spura, Thomas; Jones, Thouis R.; Cera, Tim; Leslie, Tim; Zito, Tiziano; Krauss, Tom; Upadhyay, Utkarsh; Halchenko, Yaroslav O.; Vázquez-Baeza, Yoshiki. (2020). SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods. https://doi.org/10.1038/s41592-019-0686-2.
  30. Candes, Emmanuel J; Plan, Yaniv. (2010). Matrix Completion With Noise. Proceedings of the IEEE. https://doi.org/10.1109/jproc.2009.2035722.

Downloads

Published

2026-10-28