Chronicles-OCR is the first benchmark with 2,800 images across the complete evolutionary trajectory of Chinese characters, defining four tasks to evaluate VLLMs' cross-temporal visual perception.
arXiv preprint arXiv:2401.12467 (2024)
5 Pith papers cite this work. Polarity classification is still indexing.
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Generative dictionary retrieval decodes unseen Oracle Bone Script characters at 54.3% Top-10 accuracy by synthesizing plausible variants guided by character evolution principles.
The paper introduces the S-OBI benchmark for sentence-level oracle bone inscription understanding and reports that current MLLMs remain dependent on character-level recognition due to propagating visual errors.
OracleAnalyser applies post-training and a new Stable Focal Preference Optimization algorithm to a 3B MLLM for oracle bone script analysis, releasing datasets and a benchmark where the small model outperforms larger ones.
MSLA is a new attention mechanism that models multi-scale and cross-layer interactions to achieve more accurate OBI recognition than prior attention methods.
citing papers explorer
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Chronicles-OCR: A Cross-Temporal Perception Benchmark for the Evolutionary Trajectory of Chinese Characters
Chronicles-OCR is the first benchmark with 2,800 images across the complete evolutionary trajectory of Chinese characters, defining four tasks to evaluate VLLMs' cross-temporal visual perception.
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Decoding Ancient Oracle Bone Script via Generative Dictionary Retrieval
Generative dictionary retrieval decodes unseen Oracle Bone Script characters at 54.3% Top-10 accuracy by synthesizing plausible variants guided by character evolution principles.
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Beyond Single Character: Evaluating MLLMs for Sentence-Level Oracle Bone Inscription Understanding
The paper introduces the S-OBI benchmark for sentence-level oracle bone inscription understanding and reports that current MLLMs remain dependent on character-level recognition due to propagating visual errors.
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OracleAnalyser: Analysing Implicit Semantics of Oracle Bone Scripts through MLLMs with Post-training
OracleAnalyser applies post-training and a new Stable Focal Preference Optimization algorithm to a 3B MLLM for oracle bone script analysis, releasing datasets and a benchmark where the small model outperforms larger ones.
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Enhancing Oracle Bone Inscription Recognition via Multi-Scale Layer Attention
MSLA is a new attention mechanism that models multi-scale and cross-layer interactions to achieve more accurate OBI recognition than prior attention methods.