TurkicOCR-SVTRv2-B (Quantized ONNX INT8 Model)

TurkicOCR-SVTRv2-B (INT8) is the dynamically quantized INT8 deployment graph. It delivers ~3.1× memory reduction (from 84.4 MB down to 27.1 MB) and ~2.7× faster CPU throughput (~385 lines/sec) with negligible accuracy degradation (1.93% vs 1.76% CER).

Official Links and Resources

• GitHub Repository: https://github.com/alenisaw/turkicocr
• Kaggle Models Hub: https://www.kaggle.com/models/alenissayev/turkicocr-svtrv2-b
• Hugging Face Dataset: https://huggingface.co/datasets/alenisaw/turkicocr-cyrillic
• Model Variations:
  • PyTorch FP32: https://huggingface.co/alenisaw/turkicocr-svtrv2-b
  • ONNX FP32: https://huggingface.co/alenisaw/turkicocr-svtrv2-b-onnx
  • ONNX INT8 Quantized: https://huggingface.co/alenisaw/turkicocr-svtrv2-b-int8

Quick Usage

import onnxruntime as ort

session = ort.InferenceSession("model.int8.onnx", providers=["CPUExecutionProvider"])

Citation

@inproceedings{issayev2026turkicocr,
  title={TurkicOCR-SVTRv2-B: Lightweight Line-Grounded Recognizer for Kazakh and Kyrgyz Optical Character Recognition},
  author={Issayev, Alen and Zhalgas, Aidana},
  booktitle={Analysis of Images, Social Networks and Texts (AIST 2026)},
  series={Lecture Notes in Computer Science (LNCS)},
  publisher={Springer},
  year={2026},
  doi={10.1007/978-3-031-XXXXX-X_XX}
}

License

Apache 2.0. Full code and model checkpoints available at https://github.com/alenisaw/turkicocr.