The research, conducted by Sheetal Thomas of Parul University, Manjeevan Seera of Monash University Malaysia, and Mridula Goel of BITS Pilani, K. K. Birla Goa Campus, drew on data from more than three hundred participants recruited through universities, factories, government organisations and corporate offices across four districts of Gujarat, India. Each participant completed a sixty-item personality questionnaire covering the five major domains of the Big Five framework — Neuroticism, Extraversion, Openness to Experience, Agreeableness and Conscientiousness — and copied a standard passage known as the London Letter, a text deliberately chosen because it contains a broad range of letters, words and punctuation that makes visual characteristics comparable across writers.

The handwriting specimens were then translated into numbers. A coder with roughly twelve years of specialist handwriting-analysis training, working under the Hilliger Assessment Guidelines supplied by the British Institute of Graphologists, recorded between sixteen and twenty-six binary features for each personality domain. Slant, size, spacing, pressure, margins, baseline direction, letter forms and connecting strokes were each scored as present or absent. The personality outcomes, meanwhile, were computed as continuous scores: the mean of twelve five-point Likert items per domain, yielding values on a scale from one to five rather than the binary personality labels that have complicated much of the earlier literature.

What makes this study unusual is not the data itself but the rigor of its evaluation. The researchers first audited the source workbooks, excluding sixty-two pilot-phase records and screening out incomplete, invalid or invariant questionnaires within each trait separately. That produced analytic samples ranging from 320 to 330 participants per domain. They then compared two quite different machine-learning models — elastic-net regression, a regularised linear method that shrinks weak coefficients, and histogram-based gradient boosting, a non-linear ensemble of constrained regression trees — against a deliberately humbling benchmark: a mean-prediction baseline that ignores the handwriting entirely. Every model was evaluated with five-fold cross-validation repeated five times, generating twenty-five out-of-sample test folds for each model and domain combination.

The results were unambiguous. Neither model consistently improved on the mean baseline in mean absolute error, the study’s primary metric. For Neuroticism, the baseline error was 0.3102 score units while elastic net managed 0.3104 — a difference in the fourth decimal place. Extraversion and Openness told the same story. Agreeableness was the single domain where elastic net edged out the baseline, but the advantage amounted to roughly 0.0011 score points, about one-thousandth of a single point on the five-point scale. Gradient boosting actually performed worse than the baseline in every domain. Most tellingly, mean cross-validated R² was negative for every model-domain combination, meaning that in held-out data the models predicted worse than simply predicting the training-set average.

Permutation tests reinforced the conclusion. When the researchers randomly shuffled the outcome values fifty times and re-evaluated each model, none of the observed model performances stood out from what chance would produce. The smallest permutation p-value was 0.078, for elastic net on Agreeableness, and the rest ranged far higher. The authors are careful to note that these exploratory tests used a limited number of shuffles, but taken together with the negative R² values and the negligible error reductions, the pattern points firmly in one direction: the handwriting-feature matrices contained no reliable information about the questionnaire-derived scores in this sample.

Perhaps the most interesting twist lies in what did work. Two independent, practising graphologists, blinded to all questionnaire data and to each other’s ratings, separately coded twenty-six common handwriting samples. They agreed with one another to a striking degree: mean feature-level percentage agreement ranged from 94.1 percent to 97.8 percent across domains, and mean Cohen’s kappa — a statistic that corrects for chance agreement — ranged from 0.803 for Neuroticism to 0.929 for Conscientiousness. In other words, the features could be identified reproducibly. Yet this reliability did nothing to establish validity. As the authors emphasise, inter-rater reliability and criterion validity answer different questions: one asks whether coders see the same thing, the other whether that thing predicts an independent measure of the construct. A feature can be recognised with near-perfect consistency and still carry no psychological meaning.

The study also probes the quality of the reference measures themselves. Internal consistency of the study-specific questionnaire varied substantially, with Cronbach’s alpha of 0.466 for Neuroticism and 0.675 for Openness — below the commonly cited 0.70 threshold — against 0.805 for Conscientiousness. The authors acknowledge that measurement error of this kind can attenuate any true association and cap the achievable predictive performance, so the low reliabilities limit how strongly the negative result can be generalised. But they are equally clear that weak questionnaire reliability does not rescue handwriting-based claims; it simply constrains conclusions in both directions. The study establishes that these specific coded features did not predict these specific scores, not that no conceivable handwriting representation could ever relate to a better-validated personality instrument.

The context of earlier work matters here too. Parts of the same Gujarat dataset had been used in four previous publications, covering image-based detection of Extraversion- and Neuroticism-associated features, sentiment and risk profiling from handwritten text, and questionnaire-based links between personality and mutual-fund investment preferences. The authors stress that this new paper is not a replication or repudiation of those studies but a different analytical task with a stricter estimand: predicting an independently constructed continuous personality score from the complete feature matrix, out of sample. Earlier feature-detection results, they argue, should not be read as demonstrations that handwriting can recover continuous personality scores in unseen individuals without further evidence.

The broader implications reach well beyond graphology. Personality has been linked in the behavioural-finance literature to risk tolerance, investment preferences and planning behaviour, and that association creates commercial temptation to score personality from cheap, easily collected behavioural traces — including handwriting — in finance, employment, insurance and other high-stakes settings. This study demonstrates why such shortcuts are dangerous. A technically reproducible coding pipeline feeding a properly implemented machine-learning model can still fail completely at the criterion it claims to measure, and algorithmic output lends an unwarranted air of objectivity to constructs that lack validated measurement. The authors recommend independent, preregistered, externally validated evidence, subgroup fairness analyses and clear incremental predictive value before any such tool touches consequential decisions. In publishing a transparent negative result with complete data and code — the coded features are archived on Harvard Dataverse and the full Python workflow is released — the team offers a template for how behavioural AI claims should be tested: clear outcome construction, a simple baseline, repeated out-of-sample evaluation and the honesty to report when the signal simply is not there.

Subject of Research: Predictive validity of manually coded handwriting features for questionnaire-derived Big Five personality scores

Article Title: Predictive validity of manually coded handwriting features for questionnaire derived personality scores

Article References: Predictive validity of manually coded handwriting features for questionnaire derived personality scores. (n.d.). https://doi.org/10.1007/s44163-026-02132-z

Image Credits: AI Generated

DOI: 10.1007/s44163-026-02132-z

Keywords: graphology, handwriting analysis, Big Five personality, predictive validity, machine learning, elastic net, gradient boosting, cross-validation, inter-rater reliability, negative result, psychometrics, behavioural AI

The new study, published in the journal Multimedia Tools and Applications, addresses a task known as text-independent offline writer identification. Offline means the system works from static images of handwriting — photographs or scans of paper documents — rather than from digital pen strokes captured in real time. Text-independent means the system cannot rely on recognizing specific words or characters: it must judge authorship from style alone, even when the documents being compared contain entirely different content. This is by far the harder version of the problem, and it is the version that matters most in practice, because forensic investigators, archivists, and historians rarely get to choose what text a writer happened to leave behind.

The difficulty stems from the sheer variability of human handwriting. The authors of the study point out that a person’s script shifts with age, education, emotional state, and even the intention behind the writing, while the appearance of a scanned document is further distorted by the writing instrument used and the conditions of acquisition. A ballpoint pen on glossy paper produces a different image than a fountain pen on rough fiber, even when the same hand guides both. Any system that hopes to identify writers reliably must therefore extract features that are stable across all of these nuisance factors while remaining sensitive to the deep, persistent individuality of motor habits.

The core of the proposed framework is a modified vision transformer built around what the researchers call local-global attention blocks. In a conventional vision transformer, an image is chopped into small patches, and a self-attention mechanism lets every patch exchange information with every other patch through learned projections known as queries, keys, and values. This global attention is powerful, but it has a weakness for handwriting: the most telling stylistic evidence often lives in fine local structures — the curve of a single stroke, the junction where two lines meet — and standard projection layers can blur or wash out those details. The team’s solution is to replace the standard query, key, and value projections with depth-wise separable convolutional projections, a lightweight convolutional operation that processes each channel of the patch representation independently before combining them. The effect is that the network preserves local handwriting structures while still allowing patches to communicate across the whole page.

The second innovation is a global average pooling branch woven into the architecture through a gated skip connection. Pooling across the entire feature map gives the network a compressed summary of the page’s overall writing style — spacing, rhythm, and layout tendencies that no single patch can convey. The gate, a learned switch, lets the model decide dynamically how much of this global context to blend into the local features at each block. The result is an architecture that simultaneously attends to the microscopic texture of strokes and the macroscopic character of the page, mirroring the way a human examiner might zoom in on a suspicious letterform and then step back to judge the overall flow of the script.

Getting the input right is half the battle, and the researchers designed a careful preprocessing pipeline to feed their network. Scanned pages are first segmented into individual text lines, and then words are recombined into coherent patch inputs. This matters because handwriting does not respect neat rectangular boundaries: ascenders and descenders from adjacent lines can overlap, and word spacing varies wildly between writers. By reconstructing word-level regions after line segmentation, the framework gives the transformer patches that correspond to meaningful units of writing rather than arbitrary crops, so the attention mechanism operates on fragments that actually carry stylistic information.

At the matching stage, the system condenses each document into a 512-dimensional feature representation — a numerical fingerprint of writing style — and performs page-level writer matching against a reference database. Page-level matching is a deliberate design choice. Earlier systems often compared documents line by line and then aggregated the results, but the authors show that their page-level strategy improves Top-1 accuracy, meaning the correct writer appears at the very top of the ranked candidate list more often. Intuitively, a full page offers a richer and more redundant sample of a writer’s habits than any single line, and the local-global attention architecture is precisely what allows the network to exploit that abundance without drowning in it.

The experimental evidence comes from three widely used benchmark datasets: IAM, CVL, and Firemaker. These collections span different languages, scripts, and acquisition conditions, which makes them a demanding test of generalization. IAM, drawn from English sentences copied by hundreds of writers, tests performance on unconstrained modern script. CVL concentrates on single-page samples from a large writer pool, stressing the system’s ability to discriminate when it has only limited material per person. Firemaker adds further variety in writing styles and document conditions. Across all three, the proposed framework achieved strong identification performance and consistently outperformed the line-level matching strategy, supporting the authors’ argument that page-level reasoning combined with local-global attention is the right recipe for the task.

The significance of this work extends beyond a leaderboard result. Writer identification sits at the intersection of biometrics, forensics, and cultural heritage. In forensic contexts, a robust text-independent system could help narrow suspect lists from questioned documents. In archives and libraries, it could assist in attributing unsigned manuscripts, detecting forgeries within historical collections, and tracing the hands behind medieval scripts — an application area where deep learning for writer identification has already shown promise. The architectural idea at the heart of the study, blending convolutional inductive biases into transformer attention, also reflects a broader trend in computer vision, echoing hybrid designs explored in models such as LeViT, Swin Transformer, and RegionViT, which similarly reconsider where pure global attention helps and where local structure must be protected.

There are, of course, limits to what the published results establish. The study reports performance on benchmark datasets rather than on casework material, and real forensic documents introduce degradations — folds, stains, partial pages, mixed writers — that benchmarks only approximate. Handwriting also changes over a person’s lifetime, and the authors themselves enumerate the subjective and objective factors that make the problem hard. Still, the framework’s design choices are well motivated and its evaluation spans three independent datasets, which lends credibility to the central claim: that depth-wise separable convolutional projections and gated global context give transformers the sensitivity handwriting demands. As handwriting analysis joins the growing list of domains being reshaped by attention-based models, this study offers a concrete demonstration that the architecture of the AI era can be tuned to one of humanity’s oldest biometric traits — the way each of us, uniquely, puts pen to paper.

Subject of Research: Text-independent offline writer identification using a transformer with local-global attention blocks

Article Title: Text-Independent Offline Writer Identification with Local–Global Transformer Attention

Article References: Ri, C.-Y., Choe, K.-H., Ri, M.-G., Choe, H.-S., & Ri, S.-J. (2026). Text-Independent Offline Writer Identification with Local–Global Transformer Attention. Multimedia Tools and Applications, 85(10), Article 797. https://doi.org/10.1007/s11042-026-21932-0

Image Credits: AI Generated

DOI: 10.1007/s11042-026-21932-0

Keywords: writer identification, handwriting analysis, transformer, vision transformer, self-attention, biometrics, forensics, deep learning, computer vision, IAM dataset, pattern recognition, local-global attention

The difficulty of the task stems from two opposing phenomena that researchers call intra-person variation and inter-person similarity. No one signs their name exactly the same way twice; mood, speed, writing surface, fatigue and even the angle of the pen introduce variation between two genuine signatures from the same hand. At the same time, two different people—especially those trained in the same script or school of penmanship—can produce signatures that look strikingly alike. A verification system must therefore walk a narrow line: it must tolerate the natural drift within one person’s signatures while remaining sensitive enough to reject a skilled forgery by someone else. Features that fluctuate wildly between genuine samples inflate false rejections, while features shared by many writers invite false acceptances.

To resolve this tension, the team proposes a Multi-Phase Fusion Architecture that builds reliability into the system at several successive stages rather than relying on a single classification step. The central idea is to identify a compact set of consistent and stable discriminatory features—measurements that repeatedly appear, with little variation, across the genuine signature samples of the same individual. The authors contend that such stability-driven features, being both repeatable and influential in separating writers, are the true foundation of a verification system that generalises beyond the data it was trained on, a property that matters enormously when signatures arrive in the real world under conditions no training set can fully anticipate.

The first phase of the architecture tackles the question of what to measure. The researchers combine global texture features, which characterise the overall statistical appearance of the signature image, with fine-grained texture features that capture local, detailed patterns of ink and stroke structure. Texture analysis has a long pedigree in image processing, and descriptors derived from the spatial distribution of grey-level values can encode subtle regularities in pen pressure, stroke density and slurry of ink that are difficult to articulate but easy to compute. By fusing coarse and fine scales of description, the method aims to preserve both the gestalt of a signature—its overall shape and balance—and the microscopic texture cues that often betray a forgery, such as hesitation marks, tremor or unnatural stroke filling.

The second phase is where the study makes its most distinctive contribution. Rather than feeding the full fused feature vector into a classifier, the team subjects it to multiple feature selection techniques, spanning both filter-based methods, which rank features by intrinsic statistical properties, and wrapper-based methods, which evaluate subsets of features by how well a learning algorithm performs with them. Crucially, the authors place heavy emphasis on stability analysis. A feature selection method is only useful, they argue, if it repeatedly selects the same features when the training data changes. Stability metrics are therefore computed to evaluate the consistency and robustness of the selected features across varying datasets. This step directly addresses a well-known weakness of wrapper methods: on small or noisy datasets they can latch onto accidental correlations that vanish on new data. By demanding that the chosen features remain stable, the study seeks a feature set that reflects the true, writer-specific signature of a person’s hand rather than the quirks of one particular sample collection.

The third phase turns to decision-making. Instead of trusting a single classifier, the system employs an ensemble learning technique, combining the judgments of multiple learners into a final verdict. Ensemble methods, which have a rich theoretical grounding in the pattern recognition literature, reduce the risk that one model’s idiosyncratic errors will dominate the outcome. In the context of signature verification, where the consequences of a false acceptance can be financial fraud and those of a false rejection can be inconvenience and mistrust, this layered approach to reliability—stable features first, robust selection second, collective decision third—forms a coherent defence against the two classic failure modes of biometric systems.

The evaluation is unusually broad in linguistic scope. The proposed method was tested on four multilingual signature datasets: CEDAR, MCYT-75, UTSig, and BHSig260, the last covering both Hindi and Bengali scripts. These benchmarks collectively span English, Spanish, Persian, Hindi and Bengali writing traditions, exposing the system to radically different scripts, stroke conventions and cultural signing habits. The reported accuracies are 100 percent on CEDAR, 96.61 percent on BHSig260, 88.75 percent on MCYT-75, 92.66 percent on UTSig, and 98.94 percent on the respective datasets evaluated. The authors interpret this spread as evidence of superior performance and strong generalisation capability across diverse linguistic and writing styles, with the perfect score on CEDAR demonstrating how well the stability-selected features behave when intra-writer consistency is high.

The significance of the stability-first philosophy extends beyond signature verification. Feature selection stability has become a recognised concern across machine learning, from text classification to anomaly detection, because unstable selections undermine reproducibility and trust. By explicitly measuring whether the same features survive across datasets, this study aligns signature verification with a broader movement in applied machine learning that prizes interpretability and robustness over raw benchmark scores. A compact, stable feature set is also cheaper to compute and easier to audit than a deep network’s latent representations—an attractive property for forensic and banking applications, where explaining why a signature was rejected can be as important as the rejection itself.

The work also arrives at a moment of rapid change in the field. Recent literature on offline signature verification is dominated by deep learning approaches: Siamese networks that learn writer-independent similarity metrics, transformer architectures, graph neural networks, diffusion models that generate synthetic training data, and hybrid designs that fuse hand-crafted features with learned ones. Against this backdrop, the Mysuru team’s contribution is a reminder that classical texture descriptors, careful feature selection and ensemble voting remain competitive when they are engineered around the specific statistical structure of the problem—in this case, the search for features that are consistent within writers and discriminative between them. The study builds on the authors’ own 2024 multi-dimensional review of handwritten signature verification, which catalogued the strengths and gaps of the field and evidently shaped their decision to target stability as the central design criterion.

Practical deployment still faces hurdles that no single paper can clear. Skilled forgeries continue to improve, adversarial attacks tailored to defeat verification models have been demonstrated in recent research, and real-world documents introduce noise, skew and partial signatures that laboratory benchmarks only approximate. Yet the core insight of this study—that the quest for a reliable signature verifier should begin with the question of which measurements are trustworthy in the first place—offers a durable template. If a feature is not stable across the genuine samples of the same writer, no amount of classifier sophistication can rescue it; if it is stable, even relatively simple ensembles can achieve near-perfect discrimination. As institutions worldwide continue to rely on the humble handwritten signature as a legal instrument, research that makes its automated verification more consistent, more explainable and more language-independent brings a centuries-old human practice a decisive step closer to the standards of modern biometric security.

Subject of Research: Stability-driven feature selection for offline handwritten signature verification

Article Title: A diversified study to explore consistent features across writers for offline signature verification

Article References: S D, B., R K, B., & K S, M. (2026). A diversified study to explore consistent features across writers for offline signature verification. Multimedia Tools and Applications, 85(9), Article 729. https://doi.org/10.1007/s11042-026-21883-6

Image Credits: AI Generated

DOI: 10.1007/s11042-026-21883-6

Keywords: offline signature verification, biometrics, feature selection, feature stability, texture features, feature fusion, ensemble learning, machine learning, forgery detection, CEDAR dataset, BHSig260, multilingual signatures