A comprehensive Persian offline handwritten database for investigating the effects of heritability and family relationships on handwriting

Abbas Zohrevand; Javad Sadri; Zahra Imani

REVIEW 4 major objections 6 minor 58 references

A comprehensive Persian offline handwritten database for investigating the effects of heritability and family relationships on handwriting

T0 review · 4 major / 6 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read A new handwriting database records family trees for 2,128 Persian writers, enabling tests of whether handwriting is inherited.

desk verdict A genuinely new family-relationship-labeled handwriting database, but the similarity evidence is anecdotal and the relationship labels rely on untested self-report. read the letter →

arxiv 2509.03510 v1 pith:T7XVI2A7 submitted 2025-09-03 cs.CV

Abbas Zohrevand , Javad Sadri , Zahra Imani This is my paper · ORCID

classification cs.CV

keywords offlinehandwritingrecognitionPersianhandwrittendatabaseheritabilityoffamilyrelationshipcodingwriteridentificationdirectionalgradientfeaturesimilarity

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper's contribution is a resource: the first handwriting database designed so that family relationship is part of the ground truth for every sample. It gathers digits, alphabet letters, geometric shapes, and free copies of the same Persian paragraph from 2,128 members of 210 families, and records each writer's relation to a chosen family 'center' with a compact code. The authors also run a feasibility experiment on the free texts using directional gradient features and Euclidean distance; in every family they can find a relative whose handwriting resembles the center's, which they read as evidence that family-level similarity is detectable in the data. If the database is sound, it matters because applications such as writer identification, signature verification, and forensic document examination currently treat handwriting similarities as individuality signals, without a way to control for family resemblance or to study it.

What carries the argument

The load-bearing mechanism is the center-based family-relationship coding schema. Each family is drawn as a tree hung from one designated 'center'; first-degree relations get single codes (0 center; 1 mother; 2 father; 3 sister; 4 brother; 5 daughter; 6 son; 7 wife; 8 husband), and more distant relatives are encoded by chaining these with underscores and dot-multiplicity markers—for example '0_2.1_4.1' names the center's father's first brother. This code is baked into every extracted image file name, so any digit, letter, shape, or text can be queried by kinship. Around this, the paper wraps a form design with corner markers that allow skew correction and field extraction, and a preliminary

What would settle it

Take a random subset of writer pairs labeled as close relatives in the database and test their actual genetic relatedness with documented family records or DNA markers; if a substantial fraction of high-similarity pairs are not biologically related, or if unrelated control pairs matched for education, age, and region show the same feature distances as declared relatives, the paper's central premise—that the database measures family/genetic effects—fails.

Watch

Extended reading notes

Core claim

The central claim is that a handwriting database can be, and has been, built in which family relationship is a first-class ground-truth attribute for every sample. For each of 210 families, one 'center' person recruited relatives, and all of them completed two specially designed forms; members with no common genetic roots with the center were excluded. The database contains 21,280 digits, 68,096 alphabet letters, 17,024 geometric shapes, and 2,128 free-text paragraphs, each stored in true-color, grayscale, and binary formats. Every extracted image is named with a code that encodes the writer's relationship to the center, such as '0_2.1_4.1' for the center's father's first brother. Using dire

Load-bearing premise

The database's entire purpose rests on the assumption that the self-reported family-tree codes match actual biological relatedness; the paper excludes non-genetic relatives from collection but does not verify pedigrees or DNA, so if those labels are wrong, every heritability conclusion built on the data collapses.

Editorial extensions

If this is right

Writer-identification and verification systems can now be evaluated on the harder case of distinguishing relatives, using the relationship labels to measure false-match rates among family members.
Forensic document examiners gain a public benchmark for asking whether a disputed sample could have been produced by a different member of the same family.
The relationship-coding protocol is script-independent, so equivalent family-aware handwriting databases can be built for Arabic, Latin, or other scripts and compared.
Since centers wrote the same forms three times over consecutive months, the database also enables separating within-writer variability from between-relative variability.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The strongest untested version of the paper's own goal would compare declared relatives with matched unrelated writers from the same region, school, and age; without such a control, the observed family resemblance could be caused by shared environment and handwriting instruction rather than shared genes.
The relationship codes are self-reported and explicitly exclude non-genetic relatives, but the paper provides no pedigree or DNA verification; before this dataset is used to claim heritability, a validation subset checking a sample of the coded links would be needed.
A direct heritability analysis using the coding schema's degree of kinship (siblings vs. cousins vs. grandparent–grandchild) could turn the database from a demonstration of family resemblance into an estimate of how resemblance scales with genetic distance.
Because the same paragraph was copied by all writers, controlling content makes the feature comparison clean, but it also conflates copying skill with intrinsic handwriting; an online tablet version, which the authors mention as future work, could add kinematic features that distinguish them.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

This one is unusual: the contribution is the database, not the experiments. The family-relationship coding schema (e.g., '0_2.1_4.1' for a paternal uncle relative to the center) is genuinely new as far as I can tell from their comparison table—no prior handwriting database records family links. The scale is real: 210 families, 2,128 writers, digits, letters, shapes, and paragraphs, with three image modalities and rich GT. For OCR and writer-identification researchers working on Persian, this could be a useful resource, and the multi-month re-sampling of centers (T1–T3) is a nice touch.

What the paper does well: the collection protocol is described in enough detail to be replicable; the counts in Tables 3–5 are consistent; the coding scheme is elegant and appears to reconstruct the family tree; and the comparison table is honest about prior work. I believe the novelty claim.

The soft spots are real but not fatal, in proportion.

First, the demonstration experiment is anecdotal. They compute Euclidean distances between center and family members, then show four examples of 'most similar' and 'most dissimilar' rather than reporting distributions or comparing against unrelated writers. The choice of 100 zones and Euclidean distance was made after seeing results, and the phrase 'more consistent with human eyes verification' is post-hoc. This does not invalidate the database, but the abstract's phrase 'similarities ... are detected' overstates what the experiment shows. It's more like: using a simple feature, some pairings look similar to a human.

Second, the relationship labels are self-reported. The centers were trained, and non-genetic relatives were excluded, but there is no DNA testing or pedigree audit. For studies of family resemblance (shared environment plus genes), this may be acceptable; for heritability claims, it's a serious caveat that should be stated explicitly and ideally validated on a subset.

Third, typo-level: Section 4.1 says 4,256 = 2218*2, but likely should be 2,128*2. Minor.

The paper is for the handwriting and forensic communities, especially those working in Persian and other scripts who want to ask questions about familial influence on writing. It deserves a serious referee, because the resource itself fills a gap. The referee should ask for a more rigorous validation of the relationship labels and a proper experiment with baseline comparisons before accepting broad claims about inheritance. I'd recommend sending to peer review.

Referee Report

4 major / 6 minor

Summary. The paper introduces a Persian offline handwriting database built around recorded family relationships. The resource contains handwritten digits, letters, geometric shapes, and free paragraphs from 2,128 writers belonging to 210 families, with relationship metadata encoded in filenames and ground-truth files. A family-relationship coding scheme, a form-extraction pipeline, and the database structure are described. Initial experiments compare within-family handwriting using DGF features and Euclidean distance on text paragraphs, and the most/least similar pairs are shown for four families. The authors claim this is the first comprehensive database enabling research on heritability and family effects on handwriting.

Significance. If the resource is as described, it is a genuinely useful and potentially unique contribution: a freely available multi-modal handwriting corpus with family-relationship metadata, which could open new directions in writer identification, forensic document examination, and studies of handwriting heritability. The relationship coding scheme is well organized, and the data collection effort is substantial. However, the experimental demonstration of familial similarity is currently anecdotal, and the validity of the family-relationship labels is a central risk that the manuscript does not address. These issues can be remedied, but they require substantive revision.

major comments (4)

[Section 5, Figs. 13-16] The abstract's claim that 'similarities among their features and writing styles are detected' is supported only by four selected visual examples. No aggregate distance distributions, statistical tests, confidence intervals, or comparisons against unrelated writer pairs are provided. Without a baseline, showing the most similar family member is uninformative, because a nearest neighbor exists for any set of pairs. The authors should either remove the claim or replace it with quantitative evidence, e.g., within-family versus between-family distance distributions and a permutation test.
[Sections 5.1 and 5.3] The experimental protocol contains post-hoc choices: the 100-zone split was selected 'after conducting several experiments,' and Euclidean distance was chosen because it was 'more consistent with human eyes' verification.' These choices were therefore made after seeing the results, making the demonstration potentially overfitted and not a confirmatory test. A valid protocol should preselect the feature and distance measure, or validate the choices on a held-out subset of families.
[Sections 2 and 4.2] The database's unique value rests on the accuracy of family-relationship labels, yet these labels are self-reported by 210 volunteer centers and are not independently verified. The paper states that members without 'common genetic roots' were excluded, but no pedigree documentation, DNA validation, or audit protocol is described. Systematic mislabeling (e.g., step-parent recorded as parent, adopted sibling as sibling) would invalidate downstream heritability analyses. The authors should provide a validation protocol, acknowledge this limitation, and explain the expected impact of label error.
[Sections 2, 3.3, 4.1.1, Table 3] The reported counts are internally inconsistent with the multiple-collection scheme. With 210 centers filling two forms at T1, T2, and T3, and the remaining 1,918 writers filling two forms once, the total is 5,096 forms, not 4,256 (=2,128×2). Similarly, Table 3 reports 2,128 digits per digit class, but centers wrote digits at three time points, so the per-digit count should be 2,548 unless T2/T3 samples are excluded. The typo '=2218*2' compounds the problem. All database statistics should be reconciled and rechecked.

minor comments (6)

[Section 5.2] The feature is called 'DGF' but the text repeatedly says 'DFG operator.' Please correct the typo.
[Table 4] The table appears to pair Persian letter names with incorrect glyphs: 'Che' is shown with آ, 'Yeh' with ش, 'Shin' with ق, 'Ghaf' with ه, 'He' with ی, and 'Alef' with چ. If this is only a table-layout error, it should be fixed; if it reflects the actual labeled data, it is a serious labeling error.
[Section 4.2] 'Grand-Truths' in the section title is a typo for 'Ground-Truths.'
[Section 7] 'compressive publicly available database' should be 'comprehensive.'
[Section 2.1] The code for the mother is written as '0_1.1.' with a trailing dot; the encoding description should be made consistent.
[Footnote 1] The footnote says only 'A sample version of this database can be downloaded.' Please clarify whether the full database is freely available, and how researchers can obtain it.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the database is a newly constructed resource, and the initial experiments are exploratory demonstrations rather than predictions derived from their own outputs.

full rationale

The paper's central contribution is the construction of a new handwriting database with family-relationship metadata, not a derivation or prediction. The family-relationship coding scheme (§2.1) is an internal naming convention; it does not define or presuppose handwriting similarity. The initial experiments (§5) use standard DGF features and compare within-family distances; they do not fit a parameter and then predict a closely related quantity. The choices of 100 zones (§5.1) and Euclidean distance (§5.3) are described as post-experimental selections, and the results are presented only as illustrative 'similarity detection,' not as a statistically tested prediction. The self-citation to [40] for DGF is not load-bearing, since DGF is also supported by external reference [44] and is not central to the database's novelty. Concerns about unverified family-relationship labels are validity/correctness issues, not circularity: the labels are inputs, not outputs of the handwriting analysis. No step reduces a claimed result to its own input by construction.

Assumptions & free parameters 4 free parameters · 4 assumptions · 1 invented entities

The central scientific content is the database plus a coding scheme, so the main unpaid assumptions are that the family labels are biologically accurate and that the chosen image features are a valid proxy for inherited handwriting style. The experimental section adds from-free-parameters in zone count and distance measure.

free parameters (4)

Zone count for text similarity = 100 (10 x 10)
Chosen after 'several experiments' in §5.1, not fixed in advance or justified by a formal criterion.
Distance measure for similarity = Euclidean
Selected in §5.3 because it was 'more consistent with human eyes' verification', a post-hoc choice after seeing results.
DGF quantization intervals = 8 bins of pi/4
Adopted from the standard DGF method; no sensitivity analysis is given for this choice.
Gray-threshold manual fine-tuning = manual per field
Each extracted field's threshold is manually adjusted by eye (§3.2), introducing unreported per-image tuning.

assumptions (4)

domain assumption Declared family trees are genealogically accurate and correspond to genetic relatedness.
The relationship codes in §2.1 treat every encoded edge as biological; no DNA or pedigree validation is reported.
domain assumption DGF gradient-direction histograms capture handwriting style relevant to heritability.
The similarity experiment in §5.2 assumes the 800-dimensional DGF vector is a sufficient proxy for inherited writing style.
domain assumption Free copying of the same fixed text produces comparable handwriting samples.
All writers copy the same paragraph (§5.1); differences in motivation, time, and writing conditions are not controlled.
standard math Otsu binarization and Sobel gradients are valid preprocessing for Persian handwriting.
Standard image-processing tools used in §3.1-3.2; reasonable but not validated specifically on this database.

invented entities (1)

Family relationship coding schema (digits, '_' and '.' codes)
purpose: Encodes each writer's genealogical relation to the family center in image file names and metadata (§2.1, Fig. 6).
A self-defined notation with no external validation or falsifiable predictions; its value depends on adoption and on the accuracy of the underlying family trees.

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Cite this review

Pith. "Pith review of A comprehensive Persian offline handwritten database for investigating the effects of heritability and family relationships on handwriting." pith.science (2026). https://pith.science/paper/T7XVI2A7

@misc{pith2026250903510,
  author       = {Pith},
  title        = {Pith review of: A comprehensive Persian offline handwritten database for investigating the effects of heritability and family relationships on handwriting},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/T7XVI2A7}},
  note         = {Machine review of arXiv:2509.03510}
}

read the original abstract

This paper introduces a comprehensive database for research and investigation on the effects of inheritance on handwriting. A database has been created that can be used to answer questions such as: Is there a genetic component to handwriting? Is handwriting inherited? Do family relationships affect handwriting? Varieties of samples of handwritten components such as: digits, letters, shapes and free paragraphs of 210 families including (grandparents, parents, uncles, aunts, siblings, cousins, nephews and nieces) have been collected using specially designed forms, and family relationships of all writers are captured. To the best of our knowledge, no such database is presently available. Based on comparisons and investigation of features of handwritings of family members, similarities among their features and writing styles are detected. Our database is freely available to the pattern recognition community and hope it will pave the way for investigations on the effects of inheritance and family relationships on handwritings.

Figures

Figures reproduced from arXiv: 2509.03510 by the authors.

**Figure 1.** (a), (b), Two samples of handwritings of the same text written by two cousins in Persian script. There are similarities in texture, word size, and slants. To the best of the present study’s knowledge, no comprehensive handwritten database has been created for investigating the effects of inheritance and family relationships on handwriting features of family members. Many databases have been developed for evaluating … view at source ↗

**Figure 2.** The typical family tree considered in data collection. The center is in the rectangle with the blue border and all relationships are encoded with respect to the center. The red dotted rectangles show those members having no common genetic root with the corresponding center and these are excluded from the data collection. 2.1. Family relationship coding schema The center in each family is the key-point for the family… view at source ↗

**Figure 4.** The layout of Pages #1 and #2 (a and b) data [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗

Figures from the paper (5 more)

**Figure 10.** Figure 10: A binary text sample: (a) Persian typewritten text, (b) Handwritten sample of (a). All the participants freely c [PITH_FULL_IMAGE:figures/full_fig_p021_10.png]

**Figure 11.** Figure 11: Fig.11. The main steps of the experiments for each family [PITH_FULL_IMAGE:figures/full_fig_p023_11.png]

**Figure 12.** Figure 12: Fig.12. The main eight directions (four orientations) and their quantization intervals of size pi/4 for the DGF. [PITH_FULL_IMAGE:figures/full_fig_p024_12.png]

**Figure 14.** Figure 14: Three samples of handwritten text from Family#00 [PITH_FULL_IMAGE:figures/full_fig_p026_14.png]

**Figure 15.** Figure 15: Three samples of handwritten text from Family#0 [PITH_FULL_IMAGE:figures/full_fig_p027_15.png]

Discussion (0). Continue with ORCID to comment.

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Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.

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