OmniHandwritingOCR: A Diagnostic Benchmark for Evaluating Multimodal LLMs in Handwritten OCR Scenarios

WildHandBench: A Benchmark for Handwritten Text Understanding that Challenges MLLMs and Humans

While the top model on OmniDocBench now reaches 96.34% overall on printed-document parsing, the ability of current models to handle challenging handwritten documents remains largely uncharacterized. Existing benchmarks focus on isolated text or formulas, overlook handwritten tables and real-world degradation, and report aggregate accuracy without explaining why models fail. We present WildHandBench, a benchmark containing 500 handwritten documents across three structures (free text, tables, formulas), four languages, and nine real-world scenarios. We introduce a Prior-Driven Error (PDE) metric that quantifies whether errors originate from language priors rather than visual evidence. Evaluating 18 state-of-the-art models together with calibrated human baselines, we find: (1) the best model achieves only 71.85% overall; (2) humans outperform all models yet the gap is narrow (77.09% vs. 71.85%); and (3) model errors are qualitatively different from human errors -- 63-91% of model errors are prior-driven versus only 49% for humans, exposing systematic reliance on language priors that conventional accuracy metrics cannot capture.

Extraction of Handwritten and Printed Cyrillic Text from Documents: A Resource-Efficient Pipeline

The digitisation of historical, administrative, and personal documents in Bulgarian faces considerable challenges due to the lack of robust Optical Character Recognition (OCR) and Handwritten Text Recognition (HTR) systems tailored for the Cyrillic alphabet. While modern Vision-Language Models (VLMs) and large transformer-based architectures achieve state-ofthe-art results, their performance and resource efficiency on low-resource languages remain prohibitive for decentralised, privacy-preserving applications. In this paper, we present a comprehensive, resource-efficient pipeline for extracting printed and handwritten Cyrillic text. Our system integrates advanced image preprocessing, YOLO-based document structure and table recognition, and a Permuted Autoregressive Sequence (PARSeq) model trained specifically for Bulgarian. We generated custom synthetic Bulgarian cursive datasets to mitigate the severe lack of real-world training data. Our evaluation indicates that the specialised PARSeq model outperforms traditional OCR tools such as Tesseract and EasyOCR on our custom degraded printed test set, and provides a practical, resource-efficient baseline for handwriting recognition compared to a modern local VLM (Qwen3-VL-4B). Finally, we discuss the discrepancy between synthetic and real handwritten data, highlighting the urgent need for a standardised, annotated Bulgarian HTR dataset.

Bridging the Gap in Exam Handwritten Text Recognition: Dataset, Benchmark, and Modeling.

Handwritten text recognition (HTR) in examination scenarios has gained increasing attention for its role in intelligent grading systems. However, existing studies have not systematically modeled the complex handwriting phenomena inherent in exam settings, hindering a comprehensive understanding of the recognition challenges and limitations of current methods. Specifically, handwriting artifacts pose significant challenges to recognition models in two complementary aspects: sequentially, they disrupt the reading order and lead to non-monotonic sequences, while visually, they distort character structures and induce attention drift. To enable systematic benchmarking of exam handwriting, we first construct BNU-Exam-HTR, a large-scale dataset of handwritten exam text, and establish BNU-Exam-Benchmark, a fine-grained evaluation framework defining 12 representative challenges observed in real exam handwriting. To overcome these challenges, we further propose EduOCR, a recognition model with a collaborative dual-branch decoder. The Sequential Symbol Module (SSM) uses autoregressive decoding to handle non-monotonic sequences, while the Permutation-Aware Prediction Head (PPH) simulates artifact perturbations to guide the shared encoder in distinguishing characters from noise, thus stabilizing attention and mitigating alignment errors. Extensive experiments show that EduOCR consistently outperforms state-of-the-art HTR models, OCR tools, and multimodal large language models across all 12 challenges, demonstrating superior robustness and adaptability.

MultiGhostBench: A Multilingual Benchmark for Long-Form LLM-Generated Text Attribution under Distribution Shifts

While existing work on LLM authorship attribution (AA) has made progress, available benchmarks remain limited, often focusing on English, controlled settings, or relatively outdated models, with the few multilingual studies considering only relatively short texts. We introduce MultiGhostBench, a multilingual benchmark comprising 928 books generated by five recent LLMs across six languages and three scripts, with an average length of approximately 59K words per book. The benchmark supports evaluation under domain, author, and language shifts. Evaluation of representative AA methods shows that no single method consistently performs best across settings, and performance generally degrades under distribution shifts. Transformer-based detectors can retain generator-related information across languages, although transfer effectiveness varies by language pair, whereas statistical and fingerprint-based detectors are more language-dependent. We envision MultiGhostBench as a valuable resource for the development and evaluation of robust AA methods. The dataset and code can be found at https://github.com/GrecoMT/MultiGhostBench.

IndicPage-OCR: Robust Low-Resource Adaptation for Multi-Script Indic Handwritten Page Recognition

BanglaWild: An In-the-Wild Bengali Scene Text Recognition Benchmark for OCR and Vision-Language Models

In-the-wild Bengali scene text recognition is largely unmeasured: existing resources target handwritten documents or constrained sign-board parsing, report only aggregate edit-distance metrics, and evaluate either conventional OCR or VLMs, never both on the same in-the-wild data. To address this gap, we introduce BANGLAWILD, a benchmark of 2,535 Bengali scene text images, each paired with a verbatim gold transcription, two categorical axes, four diagnostic attributes, and an orthographically standard form where the in-image text deviates from canonical spelling. We evaluate fifteen VLMs and three conventional OCR systems under three prompting strategies, fine-tune 6 open-source models with LoRA, and complement edit-distance metrics with an LLM-as-a-Judge evaluation. Our results reveal a persistent gap in which larger models within the same family do not outperform smaller ones. Our fifteen-class error taxonomy shows that visual mis-recognition accounts for ~60% of errors in the strongest systems, while conjunct-related errors contribute under 2%, challenging a long-standing assumption in Bengali OCR research; the same visual dominant profile also holds across architectures, including the one conventional baseline that reads Bengali reliably. Prompt language mainly affects cross-script drift and LoRA reduces catastrophic failures in weak models without lifting the ceiling on already competent ones. Code and data will be publicly released.