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The Baidu Paddle team officially released version 3.0 of PaddleOCR and open-sourced it. This new version has made significant progress in text recognition accuracy, multilingual support, handwriting recognition, and high-precision document parsing, further enhancing PaddleOCR's technological strength and application value in the OCR field. Since its release, PaddleOCR, with its frontier academic algorithms and industrial implementation practices, has been loved by academia, industry, and research sectors, and is widely used in many well-known open-source projects. This release of PaddleO...

Mistral AI, an artificial intelligence company, today announced the official launch of its latest document recognition model, Mistral OCR. Hailed as the "most powerful OCR on the planet," this model has sparked significant discussion on platform X due to its exceptional performance and versatility. Mistral OCR supports precise extraction from complex PDFs, images, tables, mathematical formulas, and multilingual documents, surpassing Google Document AI and Azure OCR in both speed and accuracy.

Recently, H2O.ai announced the launch of two new visual language models aimed at enhancing the efficiency of document analysis and optical character recognition (OCR) tasks. The two models, H2OVL Mississippi-2B and H2OVL-Mississippi-0.8B, demonstrate remarkable competitiveness compared to models from large tech companies, potentially offering businesses dealing with heavy document workflows a more efficient solution.

Title: OCR 2.0: A New Generation Optical Character Recognition Model for Easy Image Text Conversion. Recently, researchers have developed a new universal Optical Character Recognition (OCR) model called GOT (Universal OCR Theory). In their paper, they introduced the concept of 'OCR 2.0' for the first time. This new model aims to combine the advantages of traditional OCR systems with the powerful capabilities of large language models. The architecture of GOT is quite advanced, featuring an image encoder with approximately 80 million parameters and 500...

Apple Inc. recently launched a significant update for its multimodal AI model MM1, upgrading it to version MM1.5. This upgrade represents more than just a simple change in version number; it is a comprehensive enhancement of capabilities, showcasing the model's stronger performance across various fields. The core upgrade of MM1.5 lies in its innovative data processing methods. The model utilizes a data-centric training approach, carefully selecting and optimizing the training dataset. Specifically, MM1.5 employs high-definition OCR data and synthetic image descriptions, along with optimizations.

In the digital age, quickly converting text content from images into editable text is a common and important need. Now, the emergence of a new optical character recognition (OCR) model called GOT (General Optical Character Recognition Theory) marks the advancement of OCR technology into the 2.0 era. This innovative model combines the advantages of traditional OCR systems with large language models, aiming to create a more efficient and intelligent text recognition tool. The GOT model employs an innovative end-to-end architecture, a design that not only saves resources but also greatly enhances performance.

Recently, a model named GOT-OCR2.0 has attracted widespread attention in the industry. This model is capable of handling not only conventional text recognition tasks but also complex content such as formulas, tables, and sheet music, making it an all-around player in the OCR field. The core advantage of GOT-OCR2.0 lies in its diverse functionality and outstanding performance. Firstly, the model primarily supports the recognition of Chinese and English characters, and can be further fine-tuned to extend to more languages. This language adaptability enables GOT-OCR2.0 to be utilized in international applications.

PaddleOCR v2.8.0发布，集前沿技术、优化项目结构、解决历史问题与增强性能于一体，树立OCR领域新标准。版本亮点包括冠军算法SVTRv2与SLANet-LCNetV2，优化算法及更新，修复关键问题，引入符合PEP518规范的教程站点。感谢社区支持，PM成员与贡献者功不可没。开发者可通过访问项目地址【https://github.com/PaddlePaddle/PaddleOCR】获取更多资源与信息。

This Github project uses a GPT model to parse PDF files, which can perfectly parse the layout, mathematical formulas, tables, images, and charts within PDFs. The average cost per page is $0.013. The steps to parse PDF files are as follows: 1. Use the PyMuPDF library to parse the PDF into non-text areas and text areas. Use the PyMuPDF library to parse the PDF into non-text areas and text areas, and then use a large visual model (such as GPT-4o) to parse and obtain a Markdown file.

1: The Baidu PaddlePaddle team has launched PP-ChatOCR, a general image key information extraction tool based on the Wenxin large model. 2: PP-ChatOCR combines OCR text recognition and large model technology to extract key information from images in various scenarios. 3: PP-ChatOCR has been launched on PaddleX, supporting high-performance deployment, and has achieved good accuracy and stability.