GLM-OCR vs PaddleOCR: A Hands-On Comparison of Two VLM OCR Models
Optical character recognition (OCR) has moved well beyond reading clean, scanned text. Vision-language models (VLMs) can now read text in images and also understand what the text means in context. But how do the popular VLM-based OCR models actually compare in practice?
In this post, we compare GLM-OCR and PaddleOCR across four real-world scenarios: a phone-captured receipt, a math formula, a pie chart, and a curved-text logo. We ran everything locally using LM Studio and its Python SDK.
What Is a VLM OCR Model?
A Vision Language Model (VLM) understands images and language together. You can ask a natural-language question about an image and get an answer back. VLM OCR models are a specialized subset that can also read and interpret the text inside an image, rather than just detecting characters.
Test Setup: Running GLM-OCR and PaddleOCR in LM Studio
Both models were run locally, so you can reproduce the test on your own machine.
- Install LM Studio by following the download steps in its documentation.
- Install the Python SDK with
pip install lmstudio. - Download the models from LM Studio's Explore tab. You'll find both full-precision and quantized variants. For GLM-OCR, we used the full-precision F16 version, which is only about 2.2 GB.
- Start the local server from your terminal. It runs on port 1234, and your Python code sends requests to that port and receives the model's responses.
- Load both models. With 8 GB of VRAM, we could keep GLM-OCR and PaddleOCR loaded at the same time.
Test 1: Receipt OCR (Phone-Captured Image)
glm-simple-ocr
paddle-simple-ocr
Input: A receipt photographed with a phone, not scanned, using a simple OCR prompt.
Results:
- GLM-OCR extracted only the most relevant part, the billing section. It seems to decide what a user most likely wants from the image.
- PaddleOCR transcribed the entire receipt, as a conventional OCR engine would.
Both models read the numbers correctly. Verdict: a tie, and the better choice depends on your goal. Choose PaddleOCR for full transcription and GLM-OCR for the key details.
Test 2: Formula OCR
glm-formula-ocr
paddle-formula-ocr
Input: The standard quadratic formula.
Results:
- GLM-OCR placed the minus sign correctly, on b only.
- PaddleOCR applied the negative sign to the entire fraction, which changes the meaning of the equation.
Verdict: GLM-OCR wins. PaddleOCR was close, but even a small error like this makes a formula wrong.
Test 3: Chart OCR
glm-charts-ocr
paddle-charts-ocr
Input: A pie chart showing student participation in sports, with the prompt "chart recognition".
Results:
- GLM-OCR understood the chart's structure and produced a readable summary of how many students play each sport (football, cricket, badminton, hockey). It also added that 10% of students don't participate in any sport. That figure wasn't stated on the chart, and since the percentages already sum to 100%, it was an unsupported addition.
- PaddleOCR struggled. It picked up the horizontally aligned labels (hockey, badminton, cricket, football) but missed the slanted or vertical text and failed to capture the percentage values properly.
Verdict: GLM-OCR wins, with a caveat. Neither result was perfect, but GLM-OCR understood the chart much better. Review its output for details it may have inferred rather than read.
Test 4: Logo and Seal OCR (Curved Text)
glm-logo-ocr
paddle-logo-ocr
Input: A logo containing curved text, with the prompt "seal recognition".
Results:
- GLM-OCR read the name, the date (14 October 2023), and the Roman numerals inside the logo.
- PaddleOCR read the general text (the name and date) but didn't pick up the text inside the logo.
Verdict: GLM-OCR wins. Curved text is hard for computer vision models, and GLM-OCR handled it better.
Summary Table
| Scenario | GLM-OCR | PaddleOCR | Winner |
|---|---|---|---|
| Receipt (phone photo) | Extracted key billing details only | Full-page transcription | Tie |
| Math formula | Correct sign placement | Misplaced negative sign | GLM-OCR |
| Pie chart | Good understanding, minor added detail | Missed non-horizontal text and values | GLM-OCR |
| Logo / curved text | Read all text, including inner Roman numerals | Read main text only | GLM-OCR |
Conclusion
Across these four scenarios, GLM-OCR outperformed PaddleOCR on formulas, charts, and logos, and the two were comparable on receipts. GLM-OCR is also small, at about 2.2 GB in F16, so it runs comfortably on modest hardware like an 8 GB VRAM GPU.
This was an informal test with one example per scenario, so results may vary on your own data. We'd recommend running both models on a sample of your documents before committing to one.
FAQs
What is a VLM OCR model, and how is it different from traditional OCR?
A VLM (Vision Language Model) OCR model understands images and language together, so it can read the text in an image and interpret it in context. Traditional OCR mainly detects and transcribes characters.
Which is better, GLM-OCR or PaddleOCR?
In our tests, GLM-OCR performed better on math formulas, pie charts, and curved-text logos. PaddleOCR misplaced a minus sign in the quadratic formula and missed non-horizontal text in the chart. Both handled receipts well, and PaddleOCR gave fuller transcription.
Can I run GLM-OCR and PaddleOCR locally, and what hardware do I need?
Yes. Both models run locally with LM Studio and its Python SDK. In our test, an 8 GB VRAM GPU was enough to load both at once, and GLM-OCR in F16 is only about 2.2 GB.