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GLM-OCR

GLM-OCR is a multilingual OCR and document understanding model from ZhipuAI, built on the GLM architecture and supporting text recognition across Chinese, English, French, Spanish, Russian, German, Japanese, and Korean. It treats OCR as a sequence generation task, enabling structured text extraction from document images and screenshots. MIT licensed.

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Use cases

  • Multilingual document text extraction from scanned PDFs
  • Structured data extraction from forms and tables in images
  • Receipt and invoice OCR for financial automation
  • Screenshot-to-text conversion for multilingual interfaces
  • Building document processing pipelines for Asian language documents

Pros

  • MIT license for broad commercial use
  • 8-language support including Chinese, Japanese, Korean in a single model
  • Generative approach handles complex layouts better than classification-based OCR
  • HuggingFace Transformers-compatible for standard inference workflows

Cons

  • Generative OCR is slower than detection-based alternatives for simple text extraction
  • Language coverage is limited to 8 languages — no support for Arabic, Hindi, or other scripts
  • Output formatting (JSON vs. plain text) requires post-processing
  • Accuracy on degraded or handwritten documents not well established
  • Large model footprint vs. specialized OCR tools like Tesseract for single-language use

FAQ

What is GLM-OCR used for?

Multilingual document text extraction from scanned PDFs. Structured data extraction from forms and tables in images. Receipt and invoice OCR for financial automation. Screenshot-to-text conversion for multilingual interfaces. Building document processing pipelines for Asian language documents.

Is GLM-OCR free to use?

GLM-OCR is an open-source model published on HuggingFace. License terms vary by model — check the model card for the specific license.

How do I run GLM-OCR locally?

Most HuggingFace models can be loaded with transformers or the appropriate framework library. See the model card for framework-specific instructions and hardware requirements.

Tags

transformerssafetensorsglm_ocrimage-text-to-textimage-to-textzhenfresrudejakoarxiv:2603.10910license:miteval-resultsendpoints_compatibleregion:us