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  1. | **Model** | **Parameters (B)** | **Main Strength** | **Special Capabilities** | **Best Use Case** |
    |----------------------|--------------------|------------------------------|-------------------------------------------------------|---------------------------------------------------|
    | olmOCR-2-7B-1025 | 7 | High-accuracy document OCR | GRPO RL training, equation/table OCR | Large-scale document pipelines, technical PDFs |
    | PaddleOCR v5/VL | 1 | Multilingual parsing (109 langs) | Text, tables, formulas, charts, dynamic visual encoder | Global multilingual OCR, efficient inference |
    | OCRFlux-3B | 3 | Markdown-accurate parsing | Cross-page merging, vLLM optimization | PDF-to-Markdown, consumer GPU friendly |
    | MiniCPM-V 4.5 | 8 | State-of-the-art multimodal OCR| Video OCR, high-resolution images, fast/deep modes | Mobile/edge OCR, video understanding |
    | InternVL 2.5-4B | 4 | Efficient OCR & reasoning | Dynamic tiling, strong text extraction | Resource-limited environments, multi-image/video |
    | Granite Vision 3.3 2b| 2 | Visual document understanding| Charts, tables, diagrams, segmentation, multi-page QA| Enterprise document extraction |
    | TrOCR Large Printed | 0.6 | Clean printed-text OCR | 16x16 patch encoder, BEiT/RoBERTa | Simple, high-quality printed text extraction |
    2025-12-27 Tags: , , by klotz
  2. Due to temporary inabilities of the models to match the real values with the predictions, random spikes can arise in the “alarm” time series. Thus, in order to make the alarm system more reliable, we use a two-level structure: this first alarm, the one defined above, is merely a warning signal and is processed again to produce a more accurate second level alarm signal. Using a Moving Aggregation node, the moving averages are calculated on a backward window of 21 samples of the level 1 alarm (warning) signals. This moving average operation smooths out all short random spikes in the level 1 alarm time series, retaining only the ones that persist over time.

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