Medieval Scripts, Character Recognition, Historical Manuscripts, Neural Networks

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LLM & AI Agent Applications with LangChain and LangGraph — Part 4 — Components of GPT
pub.towardsai.net·1h
🤖Grammar Induction
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Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition
dev.to·16h·
Discuss: DEV
🤖Advanced OCR
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CHAMMI-75: pre-training multi-channel models with heterogeneous microscopy images
arxiv.org·3d
🧠Machine Learning
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AI Has Made it Easy to Own Your Tools
jimmyhmiller.github.io·1d
🤖Archive Automation
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Deep Convolutional Networks on Graph-Structured Data
paperium.net·2d·
Discuss: DEV
📊Spectral Graphs
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Book Review: Why Machines Learn
philippdubach.com·1d·
Discuss: Hacker News
🧠Machine Learning
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Exploring TabPFN: A Foundation Model Built for Tabular Data
towardsdatascience.com·14h
📝ABNF Parsing
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AI assisted feature detection and LiDAR in archaeological heritage management [Elektronisk resurs]
libris.kb.se·6d
🏺Computational Archaeology
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The art of text (rendering) (39c3)
cdn.media.ccc.de·14h
🖋Typography
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Voices from the Past: Retrieval-Augmented Dialogues with Chinese Historical Figures
digitalorientalist.com·1d
🏛Digital humanities
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The Transformer Architecture: A Deep Dive into How LLMs Actually Work
dev.to·9h·
Discuss: DEV
📝Text Parsing
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TRUNAJOD: A text complexity library for text analysis built on spaCy — TRUNAJOD 0.1.1 documentation
trunajod20.readthedocs.io·14h
📝Parsing Grammars
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SearchResearch (12/24/25): Living in an AI world that kinda, sorta works for OCR
searchresearch1.blogspot.com·3d·
👁️OCR Evolution
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The 2025 Guide to Machine Learning
ibm.com·1d·
Discuss: Hacker News
🧠Machine Learning
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Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models
dev.to·1d·
Discuss: DEV
🧮Vector Embeddings
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Towards Ancient Plant Seed Classification: A Benchmark Dataset and Baseline Model
arxiv.org·5d
🏺Computational Archaeology
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Document Parsing with LLMs: From OCR to Structural Understanding.
alamedadev.com·3d
📋Document Grammar
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Natural language processing for word sense disambiguation and information extraction
arxiv.org·21h·
Discuss: r/compsci
📥Feed Aggregation
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The History Began from AlexNet: A Comprehensive Survey on Deep LearningApproaches
dev.to·9h·
Discuss: DEV
👁️OCR Evolution
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