Many developers and data scientists struggle to get a high‑accuracy OCR system running on complex documents without building a separate layout‑analysis pipeline. Common pain points include setting up a GPU environment, installing the right library versions, choosing the correct precision (bfloat16 vs float16), preparing realistic test data, and deciding between fast single‑view inference and detailed tiled inference for dense tables or small fonts. When moving from single images to multi‑page PDFs they also need a reliable way to rasterize pages, keep long‑range context, and avoid repetitive output during decoding.
A practical solution follows these steps. First, verify that a CUDA‑enabled GPU is available and automatically select bfloat16 if supported, otherwise fall back to float16. Install the exact dependency set: transformers==4.57.1, Pillow, matplotlib, einops, addict, easydict, pymupdf, psutil, and accelerate. Load the Baidu Unlimited‑OCR tokenizer and 3B‑parameter model with use_safetensors=True, set the model to evaluation mode, and move it to the GPU.
Create input and output folders, then generate sample pages that contain headings, paragraphs, tables, and footnotes using PIL; this gives you controllable, layout‑rich material for testing. For single‑page OCR, run the model in Gundam mode (tiled crops with base_size=1024, image_size=640, crop_mode=True) to capture fine details, and in Base mode (single 1024‑pixel view, crop_mode=False) to compare speed. Keep max_length=32768, no_repeat_ngram_size=35, and ngram_window=128 for stable long outputs.
To handle multi‑page PDFs, convert each PDF page to a 300 dpi PNG with PyMuPDF, collect the image paths, and call infer_multi() with image_size=1024 and a larger ngram_window=1024 to preserve coherence across pages. Save results, inspect the generated text/markdown/json files, and adjust the generation parameters if needed.
This end‑to‑end workflow removes the need for external layout analysis, works on both dense and clean documents, and scales from single images to lengthy PDFs while maintaining reproducibility. #AI #OCR #DeepLearning #DocumentProcessing #GPU #Python