PP-OCRv6 on Hugging Face: 50-Language OCR from 1.5M to 34.5M…

Summary

PP-OCRv6 extends PaddleOCR with a scalable family of OCR models that range from 1.5 M to 34.5 M parameters across three tiers—tiny, small, and medium. The medium and small variants handle 50 languages in a single model, covering Simplified and Traditional Chinese, English, Japanese, and 46 Latin-script languages. The architecture relies on a shared PPLCNetV4 backbone for both detection and recognition, while the detection head adopts RepLKFPN, a lightweight feature-pyramid network that uses large kernels to handle multi-scale, rotated, and low-resolution text. Recognition is performed by EncoderWithLightSVTR, which combines local context modeling with global attention to improve accuracy on dense, noisy, or multilingual crops.

On PaddleOCR’s internal multi-scenario benchmark, the medium model records 86.2 % detection Hmean and 83.2 % recognition accuracy. These figures represent gains of 4.6 and 5.1 percentage points, respectively, over the previous PP-OCRv5_server release. The design emphasis remains on producing structured, production-ready output—visualizations plus JSON—suitable for downstream tasks such as document parsing, retrieval-augmented generation, or analytics pipelines.

Deployment flexibility is provided through PaddleOCR 3.7, which exposes a unified interface supporting the default Paddle Inference engine, an ONNX Runtime path, and a Transformers backend. Corresponding model weights in safetensors, ONNX, and native Paddle formats are available on the Hugging Face Hub, allowing practitioners to match the runtime to their existing infrastructure without altering the underlying OCR pipeline.

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PP-OCRv6 on Hugging Face: 50-Language OCR from 1.5M to 34.5M Parameters

Summary

Why it matters

Foundation Models for Automatic CAD Generation

Position: Current Model Cards Are Insufficient for Downstream Governance of Open-Weight Foundation Models

Introducing OlmoEarth embeddings: Custom embedding exports from OlmoEarth Studio for downstream analysis

LFM2.5-VL-3B for Better and Faster Vision Capabilities for the Edge

CARGO-VL: Counterfactual Arbitration with Risk-Constrained Group Optimization for Vision-Language Models

RAG-TESTER: Automated End-to-End Testing of Retrieval-Augmented Large Language Models

Do VLMs Read or Rewrite? On Transcription Faithfulness in Vision-Language Models

Marking the Wrong Symptoms: Evaluating LLM Watermarks in Medical Texts

Calibrated Selective Fact-Checking via Evidence Chain Evaluation

Generative Ontology Induction: Domain-Agnostic Schema Discovery from Document Corpora Using Large Language Models

ColGraphRAG: Late-Interaction Evidence Retrieval for Multimodal GraphRAG

DrawingVQA: A Real-World Benchmark for Multi-Depth Visual-Textual Reasoning on Construction Drawings

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