Rendering PDF Files as Raster Images for Computer Vision Datasets

Shantanu Bala's blog post discusses the process of converting PDF pages into raster images for use in computer vision datasets, focusing on methods to achieve this using shell scripts or Python code. It highlights the importance of selecting an appropriate resolution to balance image quality with file size and processing speed, recommending 150-300 DPI for bounding box detection and 50-150 DPI for page classification. The post explains how to use command-line tools like ImageMagick and Python libraries such as pdf2image to perform these conversions, offering specific examples and configuration options to optimize the process. It also provides a detailed script utilizing Python's asyncio for increased throughput in processing large PDF datasets, making it beneficial for server-side operations. The article underscores the utility of these techniques in preparing PDF documents for computer vision tasks, encouraging readers to begin annotating their datasets with tools like Roboflow.