Pith Number

pith:XD5JTTSW

pith:2026:XD5JTTSWZXNE4BYMT5F73V66QM

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Probabilistic Dating of Historical Manuscripts via Evidential Deep Regression on Visual Script Features

Ranjith Chodavarapu

Evidential deep regression dates historical manuscripts to within 5 years from visual features

arxiv:2605.06475 v1 · 2026-05-07 · cs.AI · cs.CV

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Claims

C1strongest claim

On the DIVA-HisDB benchmark (150 pages, 3 medieval codices, 151,936 patches), our model scores a test MAE of 5.4 years, well below the 50-year century-label supervision granularity, with 93% of patches within 5 years and 97% within 10 years. Our approach achieves PICP=92.6%, the best calibration among all compared methods, in a single forward pass, outperforming MC Dropout (PICP=88.2%, 50 passes) and Deep Ensembles (PICP=79.7%, 5 models) at 5× lower inference cost.

C2weakest assumption

That visual script features from patches of only three codices contain sufficient information for continuous year-level regression that generalizes beyond the training manuscripts, and that the Normal-Inverse-Gamma evidential framework accurately decomposes uncertainties without post-hoc fitting issues.

C3one line summary

Evidential deep regression on manuscript images achieves 5.4-year mean absolute error in dating with superior uncertainty calibration compared to dropout and ensembles.

References

16 extracted · 6 resolved · 0 Pith anchors

[1] A. Amini, W. Schwarting, A. Soleimany, and D. Rus. Deep evidential regression.Advances in neural information processing systems, 33:14927–14937, 2020 2020

[2] A. Ciula. Digital palaeography: using the digital representation of medieval script to support palaeographic analysis. 2005. URLhttps://api.semanticscholar.org/CorpusID:113619742 2005

[3] F. Cloppet, V . Eglin, M. Helias-Baron, C. Kieu, N. Vincent, and D. Stutzmann. Icdar2017 competition on the classification of medieval handwritings in latin script. In2017 14th IAPR International Conf 2017 · doi:10.1109/icdar

[4] ImageNet: A large-scale hierarchical im age database 2009 · doi:10.1109/cvpr.2009.5206848

[5] Y . Gal and Z. Ghahramani. Dropout as a bayesian approximation: Representing model uncertainty in deep learning. Ininternational conference on machine learning, pages 1050–1059. PMLR, 2016 2016

Receipt and verification

First computed	2026-05-20T00:00:41.079538Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

b8fa99ce56cdda4e070c9f4bfdd7de830e26a7bba31042d8ae8a5473167e9b21

Aliases

arxiv: 2605.06475 · arxiv_version: 2605.06475v1 · doi: 10.48550/arxiv.2605.06475 · pith_short_12: XD5JTTSWZXNE · pith_short_16: XD5JTTSWZXNE4BYM · pith_short_8: XD5JTTSW

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/XD5JTTSWZXNE4BYMT5F73V66QM \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: b8fa99ce56cdda4e070c9f4bfdd7de830e26a7bba31042d8ae8a5473167e9b21

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "074dbccc8e7f171c78fb9c2a4f6325bf9008a437679304c31254043f00c6106c",
    "cross_cats_sorted": [
      "cs.CV"
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    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.AI",
    "submitted_at": "2026-05-07T16:00:19Z",
    "title_canon_sha256": "1eb15dde9a10f204ca0ac1094a401a1ead472250fd8d28f12aadcc8d8af33bc9"
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    "kind": "arxiv",
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}