Preparing PDFs or images for AI model input involves extracting and structuring the content in a way that the model can understand. At AEHEA, we approach this process methodically, ensuring the raw data is not only accessible but also clean, consistent, and formatted correctly for the specific type of model being used. Whether you’re training a model or using one for inference, the goal is to turn visual or unstructured content into structured data that can be analyzed, classified, or interpreted by AI.

For PDFs, the first step is text extraction. This can be done using tools like PDFMiner, PyMuPDF, or Apache Tika. These libraries extract the raw text from the PDF, often with metadata like font size, page number, or layout structure. If the PDF contains scanned documents rather than embedded text, you’ll need to use Optical Character Recognition (OCR) tools such as Tesseract or Google Vision API to convert images of text into machine-readable content. After extraction, we clean the text removing headers, footers, extra whitespace, or symbols and format it into structured formats like JSON or CSV, depending on the AI model’s requirements.

For images, the preparation depends on the goal. If the task involves object detection, image classification, or OCR, we start by resizing and standardizing all images to the same resolution, often using tools like OpenCV or PIL. We also normalize pixel values and, if necessary, annotate the images using tools like LabelImg or VGG Image Annotator. Annotations (like bounding boxes or segmentation masks) are saved in formats such as COCO or Pascal VOC for supervised learning tasks. For inference tasks, the image simply needs to be in a compatible format (like JPEG or PNG), correctly sized, and optionally compressed for faster processing.

At AEHEA, we often convert both PDFs and images into structured inputs that can be fed directly into AI workflows. We build automations that scan document libraries, run OCR in batches, and route cleaned content to classification models or natural language processors. The key is consistency AI models are sensitive to noise and irregular formatting. By ensuring all documents are processed with the same steps, we increase accuracy, reduce errors, and create a pipeline that scales easily. Preparing your inputs right is the most important step to getting meaningful, reliable results from any AI system.