GLM-OCR vs. DeepSeek-OCR

GLM-OCR represents a cutting-edge multimodal optical character recognition solution and an open-source framework that stands out by providing accurate, efficient, and comprehensive document understanding through the seamless integration of text and visual components within a unified encoder-decoder framework inspired by the GLM-V series. It incorporates a visual encoder that has been pre-trained on a vast array of image-text datasets and features an efficient cross-modal connector that feeds data into a GLM-0.5B language decoder. The system is equipped with capabilities for detecting layouts, recognizing multiple areas simultaneously, and generating structured outputs that accommodate a variety of content types, such as text, tables, formulas, and complex real-world document formats. Moreover, it utilizes Multi-Token Prediction (MTP) loss alongside advanced full-task reinforcement learning methods to improve training efficiency, enhance recognition accuracy, and foster better generalization across different tasks, ultimately leading to outstanding results in significant document understanding challenges. By employing this novel approach, GLM-OCR not only establishes new performance standards but also paves the way for future innovations in the realm of document analysis and understanding. As a result, it has the potential to revolutionize how documents are interpreted and processed in various applications.

DeepSeek-OCR is an innovative open-source framework designed to explore Contexts Optical Compression, striving to enhance the boundaries of visual-text compression while analyzing the function of vision encoders through the perspective of LLMs. This pioneering model adeptly compresses large contexts using optical 2D mapping, with DeepEncoder serving as its core engine and DeepSeek3B-MoE-A570M acting as the decoding component. By effectively maintaining low activations even with high-resolution inputs, DeepEncoder achieves remarkable compression ratios, facilitating a manageable number of vision tokens crucial for document comprehension. The framework is specifically optimized for optical character recognition (OCR) and document parsing tasks associated with images and PDFs, offering inference capabilities through either vLLM or Transformers. Users can efficiently perform image OCR with streaming outputs, manage PDFs with high concurrency, or carry out batch evaluations for benchmarking. Furthermore, DeepSeek-OCR can convert documents into Markdown format, providing the ability to conduct OCR without being limited by layout constraints, parsing figures, offering detailed descriptions of images, and identifying referenced text within images. This broad range of features not only enhances its functionality but also positions DeepSeek-OCR as an essential resource for individuals seeking sophisticated document processing solutions, making it a highly versatile tool in various applications. Additionally, its continuous evolution promises further enhancements in user experience and performance.