GLM-4.5V OCR: The Rising Star of Open-Source Multimodal Text Recognition in 2025

Introduction

In the rapidly evolving landscape of open-source AI models in 2025, GLM-4.5V, jointly developed by Zhipu AI and Tsinghua University, has emerged as a rising star in the field of multimodal text recognition. Officially released on August 11, 2025, this model adopts a 106 billion parameter Mixture of Experts (MoE) architecture and demonstrates exceptional performance in OCR tasks, setting a new benchmark for open-source OCR solutions.

What is GLM-4.5V OCR?

GLM-4.5V OCR is a text recognition solution based on Zhipu AI's GLM-4.5V multimodal large language model. This model possesses powerful visual and language understanding capabilities, capable of processing various types of visual content including images, videos, and documents, with outstanding performance in OCR tasks.

Core Features

1. High-Precision Text Recognition

• Printed Text Recognition: Accuracy over 95%, maintaining high precision across various fonts and layouts
• Handwriting Recognition: Accuracy over 85%, capable of processing various handwriting styles
• Mathematical Symbol Recognition: Accuracy over 90%, particularly suitable for educational and research applications
• Complex Document Processing: Capable of handling complex documents containing charts, formulas, and tables

2. Multilingual Support

• Extensive Language Coverage: Supports text recognition in over 50 languages
• Global Applications: Meets document processing needs across different regions and cultural backgrounds
• Mixed Language Processing: Capable of processing complex documents containing multiple languages
• Special Character Support: Supports recognition of various special characters and symbols

3. Native Multimodal Architecture

• High-Resolution Processing: Natively supports processing of images and videos at arbitrary resolutions
• Temporal Understanding: Possesses powerful video temporal understanding capabilities
• Spatial Position Awareness: Enhances understanding of spatial positions in multimodal inputs through 3D-RoPE
• Mixture of Experts Architecture: Adopts MoE architecture ensuring scalability and efficient performance

4. Open-Source Characteristics

• Fully Open Source: Model is completely open source, available on Hugging Face
• Easy Integration: Provides complete APIs and SDKs for easy developer integration
• Community Support: Has an active open-source community with continuous updates and improvements
• Local Deployment: Supports local deployment for data privacy protection

Technical Architecture and Performance

Model Architecture

• Visual Encoder: Initialized based on AIMv2-Huge, introducing 2D-ROPE and 3D convolution
• Language Decoder: Based on GLM-4.5-Air, extending 3D-RoPE to enhance spatial understanding
• Temporal Understanding: Inserts timestamp tokens after each frame's visual features
• Parameter Scale: 106 billion parameter Mixture of Experts architecture

Performance Metrics

• OCRBench Score: Achieves a high score of 86.5 in OCRBench benchmark tests
• Object Detection: Accuracy reaches 92%, capable of precisely identifying objects in images
• Scene Classification: Accuracy of 89%, effectively distinguishing different scene types
• Visual Reasoning: Accuracy of 87%, possessing the ability to understand and reason complex visual information

Application Scenarios

1. Educational Technology

• Automatic Grading: Automatically recognizes and grades student assignments, improving teaching efficiency
• Learning Assistance: Recognizes textbook content, providing intelligent learning suggestions
• Content Creation: Automatically generates teaching materials and courseware
• Examination Systems: Supports automatic grading for online examinations

2. Business Process Automation

• Document Processing: Automatically processes various business documents, extracting key information
• Quality Control: Automatically checks document quality and format standards
• Customer Service: Quickly processes documents and images submitted by customers
• Data Entry: Automates data entry and validation processes

3. Healthcare

• Medical Record Digitization: Recognizes doctors' handwritten medical records, converting to electronic format
• Examination Reports: Automatically recognizes and organizes various medical examination reports
• Prescription Processing: Recognizes handwritten prescriptions, improving medication accuracy
• Medical Imaging: Recognizes text information in medical images

4. Research and Development

• Literature Processing: Automatically recognizes and organizes research literature
• Data Extraction: Extracts key data from research reports
• Experimental Records: Digitizes experimental records and observational data
• Academic Exchange: Supports recognition and processing of multilingual academic documents

Usage Methods

1. Online Demo

Visit Zhipu AI's online demo platform, upload images, PDFs, or videos to experience the model's multimodal understanding capabilities.

2. API Calls

from zhipuai import ZhipuAI
 
# Initialize client
client = ZhipuAI(api_key="your_api_key")
 
def ocr_with_glm45v(image_url):
    """Use GLM-4.5V for OCR recognition"""
    
    response = client.chat.completions.create(
        model="glm-4.5v",
        messages=[
            {
                "role": "user",
                "content": [
                    {
                        "type": "image_url",
                        "image_url": {"url": image_url}
                    },
                    {
                        "type": "text",
                        "text": "Please recognize all text content in the image and maintain the original format and layout."
                    }
                ]
            }
        ],
        temperature=0.1
    )
    
    return response.choices[0].message.content
 
# Test usage
result = ocr_with_glm45v("https://example.com/document.jpg")
print(result)

3. Local Deployment

# Get model from Hugging Face
from transformers import AutoModel, AutoTokenizer
 
# Load model and tokenizer
model = AutoModel.from_pretrained("zai-org/GLM-4.5V")
tokenizer = AutoTokenizer.from_pretrained("zai-org/GLM-4.5V")
 
def local_ocr_processing(image_path, text_prompt):
    """Local OCR processing"""
    
    # Preprocess image
    image = load_and_preprocess_image(image_path)
    
    # Build input
    inputs = tokenizer(text_prompt, return_tensors="pt")
    
    # Model inference
    with torch.no_grad():
        outputs = model.generate(**inputs, max_length=512)
    
    # Decode results
    result = tokenizer.decode(outputs[0], skip_special_tokens=True)
    
    return result

4. Desktop Assistant Application

Mac users can download the GLM-4.5V desktop assistant for localized visual content processing.

Real-world Application Cases

Case 1: Educational Institution

A renowned university uses GLM-4.5V OCR to process student assignments, achieving 96% recognition accuracy, greatly improving grading efficiency and saving teachers 80% of grading time.

Case 2: Healthcare Institution

A top-tier hospital uses GLM-4.5V OCR to digitize doctors' handwritten medical records, achieving 88% recognition accuracy and significantly improving medical record management efficiency.

Case 3: Research Institution

A research institute uses GLM-4.5V OCR to process research literature, accurately recognizing multilingual literature content with 94% recognition accuracy.

Technical Advantages and Characteristics

Advantages

• Open Source Free: Completely open source, no payment required
• High-Precision Recognition: Achieves over 95% accuracy on various document types
• Multimodal Capabilities: Capable of processing various types of content including images, videos, and documents
• Local Deployment: Supports local deployment for data privacy protection
• Community Support: Has an active open-source community with continuous improvements

Characteristics

• Mixture of Experts Architecture: Adopts MoE architecture ensuring efficient performance
• Native Multimodal: Natively supports multimodal input without additional processing
• Temporal Understanding: Possesses powerful video temporal understanding capabilities
• Spatial Awareness: Enhanced understanding of spatial positions

Future Development Trends

1. Technological Evolution

• Accuracy Improvement: Expected accuracy to further improve to over 97%
• Speed Optimization: Processing speed will significantly improve
• Multimodal Enhancement: Support for more types of media input
• Real-time Processing: Support for real-time OCR processing capabilities

2. Application Expansion

• Industry Customization: Provide customized solutions for specific industries
• Edge Computing: Support deployment on edge devices
• Mobile Applications: Develop mobile OCR applications
• Cloud Services: Provide cloud OCR services

3. Ecosystem Development

• Developer Tools: Provide more developer-friendly tools and SDKs
• Third-party Integration: Integrate with more document management systems
• Community Building: Build a more active open-source community
• Commercial Support: Provide commercial-grade technical support

Conclusion

GLM-4.5V OCR, as Zhipu AI's important layout in the open-source OCR field, provides developers and enterprises with efficient and free text recognition solutions through its exceptional technical capabilities and completely open-source characteristics. Its recognition accuracy of over 95% and powerful multimodal processing capabilities make it an important choice in the open-source OCR field for 2025.

For users who need high-precision recognition, local deployment, and data privacy protection, GLM-4.5V OCR is undoubtedly an excellent choice worth considering. Whether for educational institutions, healthcare organizations, or research institutions, efficient document digitization and intelligent processing can be achieved through GLM-4.5V OCR, while enjoying the flexibility and customizability brought by open source.

Keywords: GLM-4.5V OCR, Zhipu AI, Open Source OCR, Multimodal Model, Text Recognition, Tsinghua University, 2025 OCR Trends