3arXiv cs.AI (Artificial Intelligence)·
Persian Pixel: Large-scale synthetic OCR dataset for Persian language released
Researchers introduce Persian Pixel, a synthetic OCR dataset of over 343,000 image-text pairs covering sentence, paragraph, and full-page layouts generated from a 7-million-word Persian corpus. The dataset addresses the scarcity of annotated Persian OCR data by modeling typographic complexities of Perso-Arabic script and applying 25+ stochastic degradation models to simulate real-world document artifacts. It is designed to train and fine-tune transformer-based OCR architectures such as TrOCR and Donut, and is released openly to support Persian document digitization research.
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IHUBERT: Persian RoBERTa-base model trained on 45GB semantically deduplicated corpus
Researchers introduce IHUBERT, a 125M-parameter monolingual Persian pretrained language model trained from scratch using the RoBERTa-base architecture on a 45GB curated subset of the Sepahr-Danesh collection (~7-8B tokens). The work features a multi-stage preprocessing pipeline including vector-database-based semantic deduplication for domain-balanced pretraining, and a 139k-vocabulary BPE tokenizer optimized for Persian morphology. IHUBERT is evaluated across seven Persian NLU benchmarks, achieving state-of-the-art results on extractive QA (PQuAD F1 88.35) and NLI (FarsTail Macro-F1 0.835). The paper contributes both a new model and a semantic deduplication methodology applicable to low-resource language pretraining.
PP-OCRv6 released on Hugging Face: 50-language OCR system from 1.5M to 34.5M parameters
PaddlePaddle has released PP-OCRv6 on Hugging Face, an OCR system supporting 50 languages with model sizes ranging from 1.5M to 34.5M parameters. The release spans a wide efficiency-accuracy tradeoff range, making it relevant for both edge and server deployment scenarios. This is a practical open-weights OCR tooling release with multilingual coverage.
GPIC: Stanford Releases 28-Trillion-Pixel Permissively Licensed Image Corpus for Visual Generation Research
Stanford Vision Lab introduces GPIC, a Giant Permissive Image Corpus of approximately 28 trillion pixels comprising 100M training, 200K validation, and 1M test images, all permissively licensed for research and commercial use. Images are captioned by a state-of-the-art vision-language model, safety-filtered, deduplicated, and hosted on Hugging Face. The release includes a benchmarking protocol for generative modeling and a reference baseline using pixel-space flow matching. The dataset addresses a key gap in scalable visual generative modeling research by providing a large, stable, and openly licensed resource.
Open Preference Dataset for Text-to-Image Generation by the Hugging Face Community
Hugging Face has released an open preference dataset for text-to-image generation, collected through community participation. The dataset captures human preference signals across image generation outputs, intended to support alignment and reward modeling research for image generation models. This contributes to the growing ecosystem of open datasets for training and evaluating generative image models.
Urdu Katib Handwritten Dataset: Historical Urdu HTR benchmark with CRNN baseline evaluation
Researchers introduce the Urdu Katib Handwritten Dataset (UKHD), the first offline Urdu handwritten text lines dataset drawn from historical Katib (scribe) materials in the Nastalique calligraphic style. The paper evaluates several CRNN-based hybrid architectures for Urdu Handwritten Text Recognition, finding that a CNN-BGRU-CTC model achieves the best character and word error rates. The work addresses a recognized gap in cursive-script HTR research caused by the scarcity of benchmark datasets for Urdu.
DeepSeek releases DeepSeek-OCR-2 vision-language model on Hugging Face
DeepSeek has released DeepSeek-OCR-2, a multilingual image-text-to-text model on Hugging Face, built on the DeepSeek-VL-v2 architecture and tagged for OCR and vision-language tasks. The model has accumulated over 1.8 million downloads and 980 likes, indicating substantial community uptake. It extends DeepSeek's multimodal model lineup with a specialized document/OCR capability.
Nemotron-Personas-Japan: Synthetic Dataset for Sovereign AI
NVIDIA has released Nemotron-Personas-Japan, a synthetic dataset hosted on Hugging Face designed to support sovereign AI development in Japan. The dataset appears to consist of persona-based synthetic data in Japanese, likely intended for fine-tuning or alignment of Japanese-language models. This release is part of NVIDIA's broader Nemotron data and model family, extending it to non-English sovereign AI use cases.
DeepSeek releases DeepSeek-OCR vision-language model on Hugging Face
DeepSeek has released DeepSeek-OCR, a multilingual image-text-to-text model on Hugging Face, built on the DeepSeek-VL-v2 architecture. The model targets OCR and image feature extraction tasks and has accumulated over 2.4 million downloads and 3,275 likes, indicating significant community uptake. This represents an open-weights multimodal release from a major Chinese AI lab.