Handwritten Mathematical Expression Recognition

Latest papers

10 papers2 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

HANS: A Handwritten Answer Sheet Dataset for Noisy Hybrid Document Parsing
Oct 8, 2026Xiazhen Wu, Wansong Qin, Yangbin Zheng +5Handwritten Mathematical Expression Recognition Document Image Understanding
Jinan University, Guangzhou, China
Cross-Domain Few-Shot Writer Adaptation for Real-World Handwritten Mathematical Expression Recognition
Sep 26, 2026Paulo Grane Gabriel Silva, Lorenz Bernard Marqueses, Joel IlaoHandwritten Mathematical Expression Recognition Optical Character Recognition
Department of Computer Technology De La Salle University Manila, Philippines
PA-CDM: Position-Aware Character Detection Matching for Evaluating Handwritten Mathematical Expression Recognition
Sep 11, 2026Shiliang LuoHandwritten Mathematical Expression Recognition Automated Evaluation
East China Normal University
OCR-EDR: Rendering-Aware Diagnosis and Repair for Closed-Loop OCR Improvement
Sep 3, 2026Linnan Zhao, Kang Liu, Hao Yu +3Handwritten Mathematical Expression Recognition Optical Character Recognition
Wechat Vision, Tencent
OmniHandwritingOCR: A Diagnostic Benchmark for Evaluating Multimodal LLMs in Handwritten OCR Scenarios
Aug 19, 2026Zinuo Guo, Min Zhang, Bo JiangHandwritten Mathematical Expression Recognition Optical Character Recognition
East China Normal University Shanghai, China
A Mathematical Forum Platform for Collaborative Problem Solving and Dataset Generation for AI Reasoning
Jun 11, 2026Akbar Erkinov, Nurmukhammad AbdurasulovHandwritten Mathematical Expression Recognition
1Independent Researchers, San Francisco, CA, USA
When VLMs 'Fix' Students: Identifying and Penalizing Over-Correction in the Evaluation of Multi-line Handwritten Math OCR
Apr 1, 2026Jin Seong, Wencke Liermann, Minho Kim +2Handwritten Mathematical Expression Recognition Self-Correction in VLMs
Electronics and Telecommunications Research Institute, Republic of Korea
A Hybrid Vision Transformer Approach for Mathematical Expression Recognition
Mar 9, 2026Anh Duy Le, Van Linh Pham, Vinh Loi Ly +3Handwritten Mathematical Expression Recognition Document Image Understanding
Viettel Cyberspace Center, Viettel Group, Vietnam. Lot D26 Cau Giay New Urban Area, Yen Hoa Ward, Cau Giay District, Hanoi, Vietnam. · Faculty of Computer Science & Engineering, Ho Chi Minh City-University of Technology (HCMUT), 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City, Vietnam. · Vietnam National University Ho Chi Minh City, Linh Trung Ward, Thu Duc District, Ho Chi Minh City, Vietnam.
Texo: Formula Recognition within 20M Parameters
Feb 19, 2026Sicheng MaoHandwritten Mathematical Expression Recognition Optical Character Recognition
LTCI, T´el´ecom Paris, Institut Polytechnique de Paris Paris, France
GryphOne: Symbol-Aware Masked Diffusion for Structural Refinement in Offline Handwritten Mathematical Expression Recognition
Feb 3, 2026Takaya Kawakatsu, Ryo IshiyamaHandwritten Mathematical Expression Recognition Optical Character Recognition
Preferred Networks, Inc., Otemachi, Tokyo, Japan · Kyushu University, Fukuoka, Japan

Oct 8, 2026J/K move · Enter open · S save

HANS: A Handwritten Answer Sheet Dataset for Noisy Hybrid Document Parsing

Xiazhen Wu, Wansong Qin, Yangbin Zheng, Liangda Fang+4

Jinan University, Guangzhou, China

Handwritten Mathematical Expression Recognition Document Image Understanding Optical Character Recognition Document Layout Analysis

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Intelligent grading and automated scoring technologies constitute critical infrastructure for smart education. However, existing document parsing and handwriting recognition benchmarks are predominantly designed for well-structured printed documents or isolated mathematical expressions, lacking datasets that capture the complex characteristics inherent to student answer sheets, including multi-line derivation processes, heterogeneous mixtures of text and mathematical formulae, and noise artifacts such as strikethroughs. To address this gap, we introduce HANS, the first dataset explicitly constructed for real-world educational scenarios, encompassing mathematical expressions, natural language text, hand-drawn tables, and diverse noise patterns including corrections and deletions, accompanied by fine-grained annotations that establish a reliable foundation for robust recognition research. Building upon HANS, we propose NA-GOT, an end-to-end framework that achieves two-stage noise suppression through a lightweight noise suppression module operating at the feature level, complemented by a noiseaware attention mechanism incorporated into the decoding stage. Experimental results demonstrate that HANS poses substantial challenges to existing methods, while NA-GOT achieves significant improvements in both accuracy and stability for answer process recognition. The dataset will be made publicly available upon publication.