Model card
TrOCR-base-printed is a transformer-based optical character recognition (OCR) model designed specifically for printed text. Unlike traditional OCR pipelines that rely on separate text detection and recognition stages, TrOCR leverages a vision transformer (ViT) encoder and a language model decoder to map image patches directly to text sequences. This end-to-end architecture makes it particularly effective for high-accuracy transcription of documents, labels, and digitized archives. For developers, it offers a streamlined integration path via the Hugging Face ecosystem, providing a robust alternative to Tesseract or cloud-based OCR APIs when local deployment and Apache-2.0 licensing are priorities.
Model files and versions
Download this model
We recommend using the ModelScope CLI or SDK. Install ModelScope first, then choose a full snapshot, single file, SDK or Git LFS workflow.
microsoft/trocr-base-printedInstall the CLI and SDK dependency before downloading.
pip install modelscopeDownload the complete weights, configuration and model card.
modelscope download --model microsoft/trocr-base-printedREADME.md is used as an example; replace it with another repository file when needed.
modelscope download --model microsoft/trocr-base-printed README.md --local_dir ./dirUseful in Python projects and automation scripts.
from modelscope import snapshot_download
model_dir = snapshot_download('microsoft/trocr-base-printed')Make sure Git LFS is installed correctly.
git lfs install
git clone https://www.modelscope.cn/microsoft/trocr-base-printed.gitFetch the repository structure first, then pull large files when needed.
GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/microsoft/trocr-base-printed.gitHow to use
- 01Step 1
Read the model card and source information.
- 02Step 2
Start with a small, non-sensitive evaluation.
- 03Step 3
Review quality, licensing and usage limits.
- 04Step 4
Adopt it only after validation.
Discussions
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