dots.ocr provides efficient solutions, based on a unified visual-linguistic model (VLM) with 1.7B parameters, optimized for efficiency and accuracy by:
- single-model architecture (SMA): Use a single model to complete layout detection and content recognition, avoiding the performance loss of traditional multi-model pipelines.
- Cue Switching Technology: Switching tasks by changing the input prompt (e.g. prompt_ocr or prompt_layout_only_en) without reloading the model
- Multi-language optimizationBuilt-in support for 100 languages, specially optimized for low-resource languages to ensure accurate parsing.
- fast inference: Compact model design achieves SOTA performance in OmniDocBench benchmarks, recommending vLLM deployment for optimal inference speed
This answer comes from the articledots.ocr: a unified visual-linguistic model for multilingual document layout parsingThe
May not be reproduced without permission:AI productivity tools " How to optimize the efficiency and accuracy of multilingual document parsing?
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