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A latent diffusion model pretrained on English handwriting generates legible style-matched Ukrainian text after direct retraining on a new Cyrillic dataset.

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2026-06-29 18:42 UTC pith:4FYIUPK4

load-bearing objection The paper releases a new Ukrainian handwriting dataset and shows an unmodified DiffusionPen can be retrained on it for Cyrillic, but the evaluation stays purely qualitative. the 2 major comments →

arxiv 2605.27487 v1 pith:4FYIUPK4 submitted 2026-05-26 cs.CV cs.AI

Diffusion-Based Ukrainian Handwritten Text Generation with Cross-Domain Style Transfer

Andrii Ahitoliev , Pavlo Berezin This is my paper
classification cs.CV cs.AI
keywords handwritten text generationdiffusion modelsstyle transferUkrainian handwritingCyrillic scriptcross-domain transferlatent diffusionwriter style
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper builds a dataset of 126177 Ukrainian handwritten word images drawn from 308 writers through connected-component segmentation, quality filtering, and oversampling of rare characters. It applies this data to retrain DiffusionPen, a model originally developed for Latin scripts, without any architectural modifications. Tests across cross-lingual transfer from English samples, zero-shot use on a historical manuscript, and few-shot adaptation to modern writers produce outputs that remain legible and preserve individual writing styles. The results indicate that the underlying few-shot latent diffusion approach extends to non-Latin scripts. The authors release the dataset, models, and evaluation protocol to support further work on underrepresented writing systems.

Core claim

Retraining DiffusionPen on the constructed Ukrainian dataset without architectural changes yields legible, style-consistent word images in cross-lingual, zero-shot historical, and few-shot contemporary settings, showing that few-shot latent diffusion models generalize beyond the Latin-script domain.

What carries the argument

DiffusionPen, a MobileNetV2 triplet-loss style encoder paired with a CANINE-conditioned latent diffusion U-Net that encodes writer style and generates conditioned handwritten word images.

Load-bearing premise

The connected-component segmentation, quality filtering, and targeted oversampling steps create a representative unbiased dataset that permits successful direct retraining of the Latin-pretrained model on Cyrillic.

What would settle it

If the retrained model produces outputs that are largely illegible or fail to preserve target writer styles in the three evaluated transfer settings, the claim of successful generalization to Cyrillic would not hold.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • Cross-lingual transfer from IAM English handwriting samples succeeds on the Ukrainian data.
  • Zero-shot transfer generates usable images from an early 20th-century Ukrainian manuscript.
  • Few-shot adaptation reproduces the styles of contemporary Ukrainian writers.
  • No architectural modifications or extra adaptation techniques are required for the script change.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same dataset-construction and direct-retraining approach could be tested on other non-Latin scripts such as Arabic or Devanagari.
  • Handwriting style features captured by the encoder may prove largely independent of the underlying script alphabet.
  • Smaller or less curated target-language datasets might still suffice if the Latin pretraining already supplies robust style priors.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 0 minor

Summary. The manuscript constructs a 126,177-image Ukrainian handwritten word dataset from 308 writers via connected-component segmentation, quality filtering, and oversampling of underrepresented characters. It retrains the DiffusionPen architecture (MobileNetV2 triplet-loss style encoder plus CANINE-conditioned latent diffusion U-Net) on this dataset with no architectural changes, then evaluates cross-domain style transfer in three regimes: cross-lingual transfer from IAM English, zero-shot transfer to an early-20th-century Ukrainian manuscript, and few-shot imitation of contemporary writers. The central claim is that the resulting generations are legible and style-consistent, demonstrating that few-shot latent diffusion models generalize beyond the Latin-script domain; the dataset, models, and evaluation protocol are released as a benchmark.

Significance. If the empirical results can be substantiated with quantitative evidence, the work would be significant for establishing the first large-scale writer-labeled Cyrillic HTG resource and for providing concrete evidence that a Latin-pretrained latent diffusion model can be directly retrained for a non-Latin script without architectural adaptation, thereby supporting broader claims about cross-domain generalization in few-shot generative models for handwriting.

major comments (2)
  1. [Abstract] Abstract and Evaluation section: the central claim that the model 'produces legible, style-consistent word images' is supported solely by qualitative description; no quantitative metrics (FID, character recognition accuracy, style similarity scores, human preference rates), baselines, or error analysis are reported, and no protocol is given for how legibility or style consistency were measured, making the claim impossible to verify or compare.
  2. [Dataset construction] Dataset construction (abstract and §3): the use of connected-component segmentation to produce word-level images is presented without any validation, statistics, or visual examples confirming that the resulting samples are intact words rather than fragmented strokes or characters; in cursive Cyrillic this is a known risk, and the absence of such checks is load-bearing because the retraining success and generalization claim presuppose a representative set of complete word images.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback, which highlights important areas for strengthening the manuscript's claims. We address each major comment below and will revise the paper to incorporate quantitative evaluation and dataset validation details.

read point-by-point responses

  1. Referee: [Abstract] Abstract and Evaluation section: the central claim that the model 'produces legible, style-consistent word images' is supported solely by qualitative description; no quantitative metrics (FID, character recognition accuracy, style similarity scores, human preference rates), baselines, or error analysis are reported, and no protocol is given for how legibility or style consistency were measured, making the claim impossible to verify or compare.

    Authors: We agree that the current presentation relies on qualitative assessment and lacks the quantitative support needed for verification and comparison. In the revised manuscript, we will add FID scores computed on generated vs. real images to quantify style consistency, character recognition accuracy via a fine-tuned OCR model to measure legibility, and style similarity scores derived from the MobileNetV2 encoder embeddings. We will also include a description of the evaluation protocol, relevant baselines (e.g., direct transfer without retraining), and basic error analysis. These additions will be placed in a new or expanded Evaluation section. revision: yes

  2. Referee: [Dataset construction] Dataset construction (abstract and §3): the use of connected-component segmentation to produce word-level images is presented without any validation, statistics, or visual examples confirming that the resulting samples are intact words rather than fragmented strokes or characters; in cursive Cyrillic this is a known risk, and the absence of such checks is load-bearing because the retraining success and generalization claim presuppose a representative set of complete word images.

    Authors: We acknowledge that explicit validation of the connected-component segmentation step is necessary, particularly given the cursive nature of Ukrainian handwriting. In the revision, we will add: (i) statistics on word image dimensions, character counts per word, and writer distribution; (ii) visual examples of segmented word images in the main text or supplementary material; and (iii) results from a manual review of a random subset (e.g., 500 images) confirming that >95% represent complete words. These details will be incorporated into §3 to substantiate the dataset quality. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical retraining and evaluation

full rationale

The paper constructs a Ukrainian word dataset via connected-component segmentation, quality filtering and oversampling, then retrains the existing DiffusionPen architecture without modification and reports visual style-transfer results on cross-lingual, zero-shot and few-shot settings. No equations, derivations, fitted parameters or predictions appear in the provided text; the central claim is an empirical observation about output legibility rather than any quantity shown to equal its own inputs by construction. No self-citation chains or uniqueness theorems are invoked to justify the method. The work is therefore self-contained against external benchmarks and receives the default non-circularity finding.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim depends on the representativeness of the constructed dataset and the transferability of the pre-trained DiffusionPen model; no new entities are postulated and no free parameters are explicitly fitted beyond standard training.

axioms (2)
  • domain assumption The pre-trained DiffusionPen architecture requires no modification to adapt from Latin to Cyrillic handwritten text generation.
    The paper states it retrains the model without architectural modification and tests direct transfer.
  • domain assumption Connected-component segmentation combined with quality filtering and oversampling yields a high-quality, unbiased training set for Ukrainian words.
    The dataset construction method is presented as sufficient for effective training and evaluation.

pith-pipeline@v0.9.1-grok · 5751 in / 1408 out tokens · 35010 ms · 2026-06-29T18:42:17.227705+00:00 · methodology

0 comments
read the original abstract

Handwritten text generation (HTG) conditioned on writer style has been widely studied for Latin scripts, but remains underexplored for low-resource and non-Latin writing systems, leaving open how well existing models generalise beyond the Latin domain. Cyrillic, particularly Ukrainian, lacks both large-scale writer-labeled datasets and empirical evidence of such generalisation. To address this gap, we construct a Ukrainian handwritten word dataset of 126,177 images from 308 writers using connected-component segmentation, quality filtering, and targeted oversampling of underrepresented Ukrainian characters. We retrain DiffusionPen, a MobileNetV2 triplet-loss style encoder with a CANINE-conditioned latent diffusion U-Net, on this dataset without architectural modification, testing direct transfer from Latin to Cyrillic. We evaluate cross-domain style transfer in three settings: cross-lingual transfer from IAM English samples, zero-shot transfer to an early 20th-century Ukrainian manuscript, and few-shot imitation of contemporary writers. The model produces legible, style-consistent word images, indicating that few-shot latent diffusion models generalize beyond the Latin-script domain. We release the dataset, trained models, and evaluation protocol as a reproducible benchmark for writer-aware Cyrillic HTG, providing a foundation for extending stylized HTG to other underrepresented writing systems.

Figures

Figures reproduced from arXiv: 2605.27487 by Andrii Ahitoliev, Pavlo Berezin.

Figure 1
Figure 1. Figure 1: Connected-component word segmentation. Ink blobs within 8 px are merged into word groups (coloured). The N−1 widest inter-group gaps (dashed lines) define word boundaries. Balancing. Rare Ukrainian letters (ф, ґ, Щ, Є, Ц, ї) are oversampled by du￾plicating crops containing them 2–5×. The final dataset contains 126,177 word images from 308 writers. 3.2 Model Architecture We adopt DiffusionPen [22] without a… view at source ↗
Figure 2
Figure 2. Figure 2: Model architecture at inference. 3.3 Training The model is trained for 200 epochs on the 126K dataset with the standard LDM [26] noise-prediction objective. Text conditioning is dropped with proba￾bility pdrop=0.2 for classifier-free guidance (CFG [14]); style conditioning is never dropped. In inference, 50 DDIM [31] steps with CFG scale ω=5.0 are used. Train￾ing is performed on a single RTX 4090 GPU with … view at source ↗
Figure 3
Figure 3. Figure 3: Generated sentences in two writer styles. 4 Experiments 4.1 Setup All generated images are 64×256 pixels, matching the training resolution. Quan￾titative evaluation uses three metrics: Fr´echet Inception Distance (FID) [12] com￾puted on 5,000 matched writer-word pairs across all 308 writers; Learned Per￾ceptual Image Patch Similarity (LPIPS) [34] computed on the same 5,000 pairs as a secondary pairwise per… view at source ↗
Figure 4
Figure 4. Figure 4: Word-level style reproduction on seen writers. 4.6 Limitations The model is strong on most Ukrainian words and writers, but two edge cases remain. First, rare Cyrillic letters such as ґ and infrequent uppercase forms [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Cross-lingual style transfer. 5.2 Historical Archival Transfer Reference images are sourced from a digitized early twentieth-century Ukrainian manuscript archived by the Central State Historical Archives of Ukraine [5]. These images differ from training data in ink quality, paper texture, and let￾terform conventions. The generated words adopt the manuscript’s calligraphic [PITH_FULL_IMAGE:figures/full_fig… view at source ↗
Figure 6
Figure 6. Figure 6: Historical archival style transfer from an early twentieth-century Ukrainian manuscript [5]. 5.3 Unseen Contemporary Writer Transfer Reference images are drawn from the RUKOPYS dataset [33], a separately col￾lected Ukrainian handwriting corpus whose writers do not appear in the training set. The generated words capture the unseen writer’s slant, stroke weight, and letter shapes without any fine-tuning. The… view at source ↗
Figure 7
Figure 7. Figure 7: Zero-shot style transfer to an unseen contemporary writer from the RUKOPYS dataset [33]. 6 Conclusion This paper studied whether few-shot writer-aware latent diffusion models gener￾alize beyond the Latin-script domain. By constructing a 126K-sample Ukrainian word-level dataset from the UkrHandwritten corpus and retraining Diffusion￾Pen without architectural modification, we showed that the model achieves a… view at source ↗

discussion (0)

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