The blog post explores the advancements in Optical Character Recognition (OCR) technology driven by powerful vision-language models (VLMs), which have enhanced document AI's capabilities. It discusses the strengths and challenges of selecting suitable OCR models, emphasizing the benefits of open-weight models for cost efficiency and privacy. The text provides insights into the capabilities of various OCR models, such as handling complex components, supporting multiple output formats, and employing locality awareness to preserve reading order. It highlights the importance of choosing the right model based on specific use cases and offers guidance on evaluating models through benchmarks like OmniDocBenchmark and OlmOCR-Bench. The article also underscores the potential of going beyond OCR with techniques like multimodal retrieval and document question answering. Additionally, it addresses the cost-efficiency of using open-source models and the significance of open OCR datasets in advancing the field. Tools and methods for running models locally and remotely are presented, and the post concludes by encouraging further exploration of OCR and vision-language models.