#document-classification News & Analysis

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

5 articles

AINeutralarXiv – CS AI · Jun 96/10

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Page image classifier fine-tuned on century-spanning archives of scanned documents for further content-specific processing

Researchers developed an automated image classification system using fine-tuned deep learning models to categorize scanned historical documents by content type (text, tables, graphics), achieving 99.16% accuracy on Czech archaeological archives. The system successfully processed over 649,000 unlabeled pages, with RegNetY-16GF emerging as the most reliable model for production deployment due to consistent inter-model agreement.

AINeutralarXiv – CS AI · Jun 25/10

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Enhancing BiGRU with a KAN Block for Legal Document Classification and Summarization

Researchers have developed a novel neural architecture combining Kolmogorov-Arnold Networks (KAN) with BiGRU models for classifying and summarizing legal documents in multilingual, low-resource settings. Tested on Bengali, English, and transliterated Bengali legal documents from Bangladesh, the hybrid model achieved 67.96% classification accuracy while demonstrating that KAN integration improved performance by over 10 percentage points.

AINeutralarXiv – CS AI · Jun 26/10

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Multimodal Approaches for Visually-Rich Document Type Classification: A Comparative Analysis

Researchers conducted a systematic comparison of multimodal document classification approaches, evaluating transformer-based models (LayoutLMv3, Donut) against large language models (Qwen3-VL, Qwen3) on the RVL-CDIP benchmark. The study demonstrates that specialized multimodal transformers outperform LLM-based approaches for visually rich documents, with image data proving more critical than OCR-extracted text.

AINeutralarXiv – CS AI · May 276/10

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Document Classification Pattern Recognition via Information Fusion: A Systematic Review of Multimodal and Multiview Representation Approaches

A comprehensive systematic review of 139 studies reveals that multimodal information fusion improves document classification accuracy by 5.28 percentage points, while multiview approaches provide modest gains of 4.67%. The research identifies critical gaps in methodological rigor, with less than 24% of studies employing statistical validation, highlighting the need for more robust research standards in the field.

AIBearisharXiv – CS AI · May 16/10

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Beyond Accuracy: LLM Variability in Evidence Screening for Software Engineering SLRs

A comprehensive study comparing 12 large language models against 4 classical classifiers for automating evidence screening in software engineering systematic literature reviews reveals that LLMs exhibit significant performance variability and lack consistent superiority over traditional methods. The research emphasizes that abstract availability is critical for LLM performance, while title and keywords provide minimal additional value, suggesting LLM adoption should be driven by operational constraints rather than performance guarantees.

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