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FAQ

What is the Vesuvius Challenge?​

The Vesuvius Challenge is a machine learning and computer vision competition to read the Herculaneum scrolls. The scrolls were buried and carbonized by the eruption of Mount Vesuvius in 79 AD. After their discovery in the 1750s, some were opened physically, mostly destroying them but revealing some Greek philosophy and Latin works.

A few hundred scrolls were excavated that were never opened, and remain rolled up with their contents sealed away. Our community is building methods to read these scrolls using micro-CT and an algorithmic pipeline using machine learning and computer vision.

In 2023 we awarded over $1,000,000 in prizes and broke through, revealing complete passages of Greek philosophy from the inside of a closed Herculaneum scroll for the first time. Now we are continuing - we want to go from reading 5% of one scroll to reading multiple complete scrolls. Join us to win prizes and be a part of history!

What dates do I need to know?​

  • March 15, 2023 - The Vesuvius Challenge is launched.
  • February 5, 2024
    • 2023 Grand Prize awarded for recovering complete passages from inside a Herculaneum scroll.
    • Stage 2 is launched with new prizes.
  • December 31, 2024 - 2024 prizes deadline!

How can I learn more about Herculaneum, the scrolls, and this research effort?​

We have an overview page for that! For a deep dive:

Academic papers​

For a comprehensive overview of the field, see this list by EduceLab.

Talks​

Books​

The best book we have found is David Sider’s The Library of the Villa dei Papiri at Herculaneum.

Here are some other excellent books we recommend:

Herculaneum scroll reconstruction from a book by Marzia D’Angelo (source); more explanation here

Videos​

Previous media coverage​

Translations of opened scrolls​

Most are by Philodemus. This is a list of English translations we have found so far:

What if I would like to contribute, but don’t have time to compete for the Grand Prize?​

  • Join our Discord to learn about current efforts and how you can pitch in.
  • You can make smaller open source contributions, which would benefit the whole community. Everyone in the community will be grateful for your work, and you might even be able to win a prize - see those already awarded!

Can I share my progress on social media?​

Yes, in fact we encourage you to share your progress. Be sure to also post in our Discord, to get feedback from the community.

The only exception is that per the data agreement, you’re not allowed to publicly share material revelation of text (e.g. entire words) without our permission (including the associated code), or share the raw data. You are allowed to share these things on Discord, since everything uploaded on Discord falls under the same data agreement as our data server.

I’m outside the United States, can I participate and win prizes?​

Absolutely! As long as we can legally pay you (no US sanctions) you can win prizes.

Do I have to pay taxes on my prize earnings?​

This depends on the jurisdiction you live in, but generally yes, you do have to pay taxes. Consult your tax advisor.

I’m a researcher or student. Can I publish my results?​

Generally yes, with the conditions that are specified in the Data Agreement:
  • Any publications and presentations must cite the EduceLab-Scrolls Dataset.
  • You won’t publish any revelation of hidden text (or associated code) without the written approval of Vesuvius Challenge.

We very much encourage researchers and students to work on this! Be sure to reach out to us on Discord or by email.

I have made some progress, who do I inform about this?​

Please email us at [email protected]. We will keep it confidential. We appreciate you keeping us in the loop!

If you're open to sharing your improvements publicly (and be eligible for progress prizes), you can post in Discord.

Do we really need 7.91µm or 3.24µm resolution? These data files are huge!​

We don't know yet what the minimum resolution necessary to detect ink is, but this paper suggests that it may be 7.91µm: From invisibility to readability: Recovering the ink of Herculaneum.

Can machine learning models hallucinate letters that aren't there?​

This is a risk for models that are trained on letterforms. We strongly recommend that participants guard against the risk of hallucination in their models, and will review all submissions with this in mind.

What is papyrus and how is it made?​

Papyrus is a grassy reed that grows along the banks of the Nile in Egypt. It can grow up to 4.5 meters tall and 7.5cm thick. The tough outer rind is peeled away. The green inner pith is peeled or sliced into strips.

The strips are laid out in two layers in a grid pattern. They are pressed together until the layers merge like velcro. And then left out in the sun to dry, where they turn light brown. The sheets – called kollemata – are smoothed out with an ivory or seashell ruler. The kollemata are then glued together with paste made of flour and water. Then the areas where they are joined are hammered smooth. This forms a long piece of papyrus, usually 15-30 feet, comprised of up to 20 kollemata.

The Papyrus is rolled up around a dowel called an umbilicus. Portions of it are unrolled for writing. The first section, called the protokollon, is usually left blank. Text is written in columns with a quill and inkwell. Inks are made of varied substances.

There are two ways you can write on papyrus: horizontally (“volumen”) or vertically (“rotulus”). All of the Herculaneum papyri are horizontal scrolls.

Horizontal and vertical scrolls (source)
For some good videos about how papyrus is made see:

How big are the letters, and where can we expect to find text?​

Letter sizes vary, and of course we don’t know what’s inside the unopened scrolls, but we expect the opened fragments to be fairly representative. You can measure how big the letters are by looking at the aligned surface images, which have a voxel resolution of approximately 3.24µm, like the original CT data (though there can be some local variation due to the registration / flattening process). So you could open, for example, fragments/Frag1.volpkg/working/54keV_exposed_surface/ir.png, measure a letter size in pixels, and multiply by 3.24µm.

There are also some measurements in this paper by Richard Janko, though it’s a little hard to infer actual letter sizing from it. If someone wants to do a more thorough review of the range of letter sizes found in all the Herculaneum papyri, we’d happily include your results here!

From our first Q&A with the Papyrology Team, we learned (summary courtesy of Santiago Pelufo on Discord):

  • 2 orthogonal layers of fibers in a sheet.
  • ~100um sheet thickness
  • Scroll outer layer of a sheet = back of a sheet = vertical fibers.
  • Scroll inner layer of a sheet = front of a sheet with writing = horizontal fibers (text written here).
  • Sheet layers unlikely to delaminate with carbonization
  • Carbonization likely fuses multiple sheets (big issue IMO)
  • 10-20cm blank at start and end of scroll.
  • 4.5-6cm columns, 1/6 of column space between columns
  • ~1/8 height paddings top and bottom
  • Text typically written on the inside (to protect against damage), and on the side with horizontal fibers (easier to write on).

How can we get more ground truth data? Can I make my own carbonized scrolls?​

Yes! Just buy this papyrus on Amazon, roll it up, and put it inside a Dutch oven at 500F+ (260C+) for a few hours.

This is very instructive and we highly recommend doing it! You will see how fragile the charred scroll is, how it blisters in the heat, how the layers can separate, how it turns to dust as you handle it.

Of course, for it to be useful as ground truth data, you will need to find someone to let you image it in their CT scanner.

What software is available currently that might help me?​

The two main pieces of software developed by Dr. Seales’ lab are Volume Cartographer and ink-id. Both are open source and available on Github.

In the tutorials we also show you how to use generic software to work with 3D volumes (Fiji) and meshes (MeshLab).

There is a growing body of open source software now available as a result of Vesuvius Challenge. To learn more, check out the previous prizes that have been awarded to many of these efforts and our list of community projects.

Where can I find collaborators?​

In this Discord thread.

What would the papyrus scrolls look like when unrolled?​

Something like this:

Reconstructed scroll (source)

Why are there no spaces in the text?​

In ancient Latin and Greek, they didn’t use spaces! Spaces were added later to aid foreign language learners.

How does CT-scanning work exactly?​

We take X-ray photographs of the object from different angles. Typically this is done by having an X-ray source on one side of the object, and an X-ray camera on the other side, and rotating the object on a platform. If the object doesn’t fully fit in the frame of the camera, it can be moved around as well.

Just like with any digital camera, there are a lot of settings and parameters. The most important for you to know are:
  • Resolution: the dimensions of each pixel in an X-ray photo, typically denoted in µm (micrometers or “microns”). Lower is better. We scanned the scrolls at 7.91µm, which we think should be enough to detect ink patterns, but we scanned the fragments at 3.24µm just in case. Renting beam time on a particle accelerator is expensive, but if we need to we can go back and scan objects at even lower resolutions.
  • Energy level: the energy of the X-ray electrons, typically expressed in keV (kilo electronvolts). For particle accelerators this is one precise number, whereas for bench top scanners this is more of a range. We think lower is better, since carbon responds better to lower energy levels. We scanned everything twice, at 54keV and 88keV (though for the scrolls we only had time for a smaller slice at 88keV).

At high resolutions the field of view of the camera is too small to capture the object in its entirety, so multiple passes have to be made. Typically these are stitched together as part of the scanning process.

Raw X-ray photos
A fragment rotating (source)

From the X-ray photos from different angles we can reconstruct a 3D volume, using a clever algorithm called tomographic reconstruction (which is where “CT scanner” gets its name; ”computed tomography”). This is typically done by software that comes with the scanner.

Volumetric representation of a fragment, showing multiple layers of papyrus
Mesh representation of the same fragment

The resulting 3D volume is like a 3D image. Each unit is called a “voxel” (instead of “pixel”), and has a particular brightness (it’s greyscale). This 3D volume is typically represented as a “.tif image stack”. This is just a bunch of .tif images where each image (called a “slice”) represents a different layer the z-direction, typically starting at the bottom and moving upwards.

How does CT reconstruction work?​

Tomographic reconstruction is used to convert the initial X-ray projection images into the cross-sections we are used to seeing from computed tomography (CT). In our case, filtered backprojection is used as the reconstruction algorithm. In the case of scrolls scanned in the parallel beam of a synchrotron, we also use a "grid scan" technique to tile high resolution projections together and scan objects larger than the sensor field of view.

For more information about the reconstruction method, check out:

Reconstruction methods are out of scope for Vesuvius Challenge, which focuses on processing the reconstructed images. That said, if you have specific ideas you would like to share with us, please do so by reaching out to [email protected]!

How should the intensity values in the CT scans be interpreted?​

The intensity values should be considered relative: within a CT scan, a higher value indicates higher radiodensity compared to a lower value from the same scan. There are not units attached to these values that have an absolute physical interpretation, or that allow direct density comparisons between scans. These forms of data are sometimes called qualitative (for relative values) and quantitative (for absolute values with units), even though they're both "quantitative" in the sense we often think of, in that they are numerical.

Relative values like this are typical in CT due to the nature of the imaging technique. The medical CT community has a convention called the Hounsfield unit (HU) that approaches quantitative data, but has caveats. The HU is calculated based on a linear ramp using baseline attenuation measured from distilled water (defined as zero HU) and air (-1000 HU). Certain tissues then tend to occupy particular ranges, for instance bone can commonly reach 1000 HU. This can be helpful in the right application, but the HU is still considered unreliable as an absolute value, particularly between different scans.

Seth Parker described this with respect to our data using an analogy to photography:

Filtered back projection doesn't set a mean explicitly- every voxel is calculated as the weighted sum of projections of that voxel, with the weights derived analytically. So in general the intensity scale is all relative. A loose analogy here is determining the element of an object by taking its color photograph: color in the image is a function of the object's chemistry, but also the color of the incident light, ambient light bouncing around the scene, the exposure properties of the camera, the light response of the sensor/film, etc. Not only that, but multiple materials may have the same color under a specific lighting condition. If you don't have some way of disentangling those effects (for example, controlling lighting, capturing under multiple exposure conditions, having known samples in the FOV to use for calibration), then it's hard to say much beyond what the color is.

The ensuing discussion is also informative and can be found on our Discord.

Based on this, the raw reconstruction values for a scan do not have units or physical interpretations attached to them. These 32-bit float values are typically in the range [-0.1, 0.1] or smaller. For more recent scans, we are releasing .hdf files that contain these original reconstruction output float values, so you can experiment with your own intensity windowing. For the 16-bit integer .tif slices that we release, we map the float range to [0, 2^16-1] by choosing a minimum and maximum in the raw float range and scaling accordingly. The fragments and all more recent scans use the 0.01 percentile and 99.99 percentile as the window min and max. Scroll 1 and Scroll 2 use 0.1 and 99.9, to achieve visually comparable output since they have so much more papyrus in the field of view.

Reconstruction outputs should be nonnegative by the principles of backprojection (there can't be negative X-ray attenuation). But noise and other processes lead to some negative values in the reconstructions. This is typical with CT. To remove these negative values, the window min could just be clamped at zero. This would result in an image where air would be black, and there would be more visual contrast.

We did not clamp the minimum at zero, instead using percentiles. Air therefore does not appear black in the .tif slices, but is gray and has some noise. For ink detection, we are looking for something subtle, and are training models to detect it. Removing all negative values from the reconstructed image makes the output visually resemble expectations, but is inherently destructive. We don't yet know if there might be any subtle ink signal in the "noise" of the negative values, and so leave the data as unaltered as possible so the models can decide for themselves what to look for.

If you want to experiment with comparing scans across energies, there are some materials of known composition in the field of view that are consistent between scans, and you may wish to use them as a sort of baseline. For example, air is present in all scans, and the scroll cases are made of Nylon 12.

What signals might be present in the 3D X-ray scans for ink detection?​

There remain open questions, but we do know the ink is sometimes directly visible as a "crackle" pattern, a texture resembling cracked mud that appears where the ink sits proud of the surface and appears to have dried.

There may be other patterns present that are detectible by machine learning. We suspect that ink might be filling in between the grid pattern of papyrus, kind of like syrup filling in gaps in a waffle.

Syrup filling in gaps in a waffle (source)

Ink might also be sitting on top of the papyrus, causing a slight bump on the surface. In Tutorial 5 we show several examples of where the ink is directly visible in slices of 3D X-ray scans, which is promising. The talks at the top of this page also go into some details.

There might be some effect of indentation of the writing instrument, but it’s probably not very significant. The thought has generally been that any indentation effect would be even smaller than ink w.r.t. the scan resolution and maybe not significant when compared against the natural relief of the papyrus fibers. However, this has not been explored in detail on this type of material (look at the paper "Revisiting the Jerash Silver Scroll" for work on an etched metal scroll), so we don’t know for sure.

It could be worthwhile to try to reverse engineer what machine learning models are seeing, so that perhaps we can see it more directly. Perhaps this could influence other ink detection methods or future scanning efforts.

Does segmenting and flattening need to happen before ink detection?​

This ordering is largely historical and due to the way we’ve constructed label sets, which relies on doing the segmentation and flattening first. But this can’t be the only way to do it, and we’d love to see the pipeline get shaken up.

For example, the model input of ink detection could be sampled directly from the original 3D X-ray volume, instead of using a “surface volume” as an intermediate step. This could avoid loss of resolution during the sampling process into a differently oriented volume, which happens when constructing a surface volume.

The downside of such an approach is that a lot more data needs to be accessible on disk, since the original 3D X-ray volumes are much bigger than the surface volumes (37GB vs 1.6TB in total for all fragments). This can be problematic for cloud training, which might not have enough available hard drive space. However, since we only need to access the voxels around the mesh, the data size could be reduced (creating something like a surface volume, but retaining the original coordinate space, and avoiding any resampling).

Fiji/ImageJ crashes, what can I do about that?​

Fiji/ImageJ doesn’t work well with extremely large datasets such as our scrolls or fragment volumes, though downsampling might help. If you’re experiencing problems even with the campfire.zip dataset, then try to increase the memory limit: “Edit > Options > Memory and Threads”. It might also help to run the software in a different operating system, such as in a Linux VM. For example, on Windows the following setup seems to work well: WSL2, Ubuntu 20, Windows 11, using the default WSL X server setup.

A great contribution to the community would be to build an open source 3D volume viewer that is tailored to this problem. If you are interested in building something like that, do let us know in Discord!

How are the scroll slices oriented?​

Scroll 1​

The orientation of Scroll 1 follows the above image. When viewing one of the TIF cross-sections from the scan, the image number increases from the screen toward the viewer’s eye.

Based on the counterclockwise spiral direction in the middle of Scroll 1, the released scans are of the top of the scroll: Slice 0 is in the middle and Slice 14000+ is the top.

Lastly, all of the Herculaneum papyri are known to be "volumen"/horizontal scrolls (see FAQ: https://scrollprize.org/faq#what-is-papyrus-and-how-is-it-made).

Therefore, the direction of a given line of writing should be clockwise around the TIF cross-sections. The bottom of the letters should be on the lower-numbered images, and the top of the letters should be on the higher-numbered images.

Scroll 2​

Relatedly, we can likely assume handedness is consistent between the scans of Scroll 1 and Scroll 2 (the TIF cross-section image number increases from the screen toward the viewer’s eye).

There's a region in Scroll 2 where the scroll center appears to have drifted/squished its way outside of the center scanning artifact. Only a few slices are as clear as this example (Slice 4680). The center spiral of Scroll 2 appears to be clockwise, unlike Scroll 1's counterclockwise spiral.

Assuming consistent handedness, a counterclockwise spiral suggests the released half of Scroll 2 is the bottom half of the scroll: Slice 0 is in the middle and Slice 14000+ is the bottom.

The direction of a given line of writing in Scroll 2 would be counterclockwise around the TIF cross-sections, with the bottom of the letters on higher-numbered images and the top of the letters on lower-numbered images.

Scrolls 3, 4, 5​

Scrolls 3, 4, and 5 appear to be oriented like Scroll 2, with counterclockwise spirals. However, a change in scanning convention flipped the ordering of the tif stack. When viewing Scroll 3, 4, or 5 TIF cross-sections from the scan, the image number decreases from the screen toward the viewer’s eye. Therefore, the direction of a given line of writing should be counterclockwise around the TIF cross-sections. The bottom of the letters should be on the lower-numbered images, and the top of the letters should be on the higher-numbered images.

Note: This flip in the scanning convention must be accounted for in any rendering pipelines.

What happened to the people when Mount Vesuvius erupted? 😢​

We recommend starting with the only surviving eyewitness account: Pliny the Younger, Letters 6.16 and 6.20.

The story of the eruption of Mount Vesuvius has captured imaginations for centuries. The cities of Pompeii and Herculaneum are unique in how well they were preserved. A great introduction to this story is A Timeline of Pompeii.

Map showing Mt. Vesuvius in relation to Herculaneum, Pompeii, and Misenum (where Pliny the Younger witnessed the event) (source)
Some more resources:

Why did you decide to start this project?​

Nat read 24 Hours in Ancient Rome during the 2020 COVID lockdown. He fell into an internet rabbit hole that ended up with him reaching out to Dr. Seales two years later to see how he could help speed up the reading of the Herculaneum Papyri. They came up with the idea of the Vesuvius Challenge. Daniel was intrigued by this idea and decided to co-sponsor it with Nat.

I have a lot of money! Can I help sponsor this?​

Vesuvius Challenge Inc. is a 501c3 non-profit organization that was formed solely to solve the puzzle of the Herculaneum Papyri. It is currently funded by the sponsors listed on the homepage, and by many hours of volunteer contributions.

If you want to contribute money to support our operational costs or to increase the prize amounts, please get in touch!

I’m a journalist and I would like to interview someone from the Vesuvius Challenge!​

Please email [email protected].

Do you have a scroll that looks like the Nintendo logo from GoldenEye N64?​

Of course (🔊 sound on).

Do you have a super-cringe, over-the-top, and factually questionable trailer video for the competition?​

Naturally.