2026.6.11 v3.7.0 released
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Release PP-OCRv6
- Accuracy boost: Medium tier achieves +4.6% detection and +5.1% recognition over PP-OCRv5_server, surpassing mainstream VLMs (Qwen3-VL-235B, GPT-5.5) with only 34.5M parameters.
- 50 languages unified: Single model covers Chinese, English, Japanese, and 46 Latin-script languages — no model switching needed.
- Specialized scenarios: Major improvements in digital displays, dot-matrix characters, tire prints, and industrial text recognition.
- Faster inference: 5.2× CPU speedup (OpenVINO), 6.1× on Apple M4 (tiny), 0.13s on A100 GPU.
- Three tiers for all scenarios: tiny (1.5M) / small (7.7M) / medium (34.5M) for edge, mobile, and server deployment.
2026.6.11 v3.7.0 发布
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发布 PP-OCRv6
- 精度全面提升：medium 档相比 PP-OCRv5_server 检测精度提升 4.6%、识别精度提升 5.1%，以仅 34.5M 参数超越 Qwen3-VL-235B、GPT-5.5 等主流视觉语言大模型。
- 50 种语言统一支持：单一模型覆盖中文、英文、日文及 46 种拉丁语系语言，无需为不同语种切换模型。
- 专业场景增强：数码显示屏、点阵字符、轮胎印字、工业字符等传统 VLM 难以覆盖的场景识别能力大幅提升。
- 推理速度更快：medium 档 CPU OpenVINO 推理加速 5.2×，tiny 档 Apple M4 加速 6.1×，A100 上仅需 0.13s。
- 三档模型覆盖全场景：tiny（1.5M）/ small（7.7M）/ medium（34.5M）分别面向端侧/移动端/服务端部署。
Full Changelog: v3.6.0...v3.7.0