#handwriting-recognition News & Analysis

5 articles tagged with #handwriting-recognition. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

5 articles

AINeutralarXiv – CS AI · Jun 256/10

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HG-Bench: A Benchmark for Multi-Page Handwritten Answer-Region Grounding in Automated Homework Assessment

Researchers introduce HG-Bench, a benchmark dataset of 500 annotated homework samples for evaluating automated grading systems' ability to locate and decompose handwritten student answers across multiple pages. Current AI models, including frontier VLMs, achieve less than 55% accuracy on complete answer localization, revealing a significant capability gap in understanding spatial reasoning structures in handwritten documents.

AINeutralarXiv – CS AI · Jun 116/10

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Towards Fully Automated Exam Grading: Fairness-Aware Recognition of Handwritten Answers with Foundation Models

Researchers demonstrate that vision-language foundation models can achieve 98.4% accuracy in automatically grading handwritten exam answers, compared to previous methods' 88-91%. The approach prioritizes fairness by minimizing false negatives that disadvantage students and shows promise for scalable, automated exam grading without sacrificing pedagogical quality.

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AINeutralarXiv – CS AI · Jun 95/10

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Hybrid E-Assessment in Higher Education: Semi-Automated Grading of Paper-Based Written Examinations

Researchers propose a hybrid e-assessment system for higher education that combines paper-based examinations with semi-automated grading using vision-capable large language models. The approach addresses limitations of fully digital assessment while maintaining pedagogical integrity and scalability through handwritten character recognition and validation protocols.

AINeutralarXiv – CS AI · Jun 95/10

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Intelligent Character Recognition of Handwritten Forms with Deep Neural Networks

Researchers present a novel deep neural network approach that combines handwritten character detection and classification into a single task, eliminating the need for manual annotation by using synthetically generated training data. The method achieves 88.28% recognition accuracy on real exam forms, demonstrating superior performance compared to traditional two-stage approaches.

AINeutralarXiv – CS AI · May 126/10

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From Historical Tabular Image to Knowledge Graphs: A Provenance-Aware Modular Pipeline

Researchers present a modular, provenance-aware pipeline that converts handwritten archival tables into Knowledge Graphs while maintaining transparency through intermediate inspection points. The approach combines table structure recognition, handwriting recognition, and semantic interpretation while tracking data lineage to ensure all extracted information remains traceable to its source, addressing the opacity problem in end-to-end AI systems.