Unstructured data like text files and documents comprise 80% of all datasets, making robust data management solutions essential for extracting valuable insights from this vast amount of information. PDF documents are a significant source of such data, containing invoices, reports, contracts, research papers, presentations, and client briefs. Companies can use these documents to improve products and business operations by extracting relevant data and using it in machine learning (ML) models. However, PDF text extraction is complex due to the varied nature of documents. High-quality text extraction matters for robust ML models as their accuracy and reliability heavily depend on the quality of the training data. Poorly extracted text can introduce noise, such as missing characters, misaligned structure, or incorrect semantics, preventing a model's algorithms from learning hidden data patterns effectively and causing overfitting limited data samples. Accurate data extraction preserves context, structure, and meaning, producing better feature representation and model performance. AI-based methods offer a cost-effective alternative for text extraction by allowing developers to quickly extract data from various document types while ensuring consistency across the entire extraction pipeline. These methods include deep learning techniques to intelligently identify and draw out relevant information from complex, unstructured formats like PDFs, scanned documents, or images. Automated text extraction techniques include optical character recognition (OCR) and natural language processing (NLP). OCR technology is pivotal for extracting text from scanned or image-based PDFs by converting visual characters into machine-readable text. NLP techniques allow experts to extract text by enabling them to perform deeper analysis for better contextual understanding, including named entity recognition, sentiment analysis, part-of-speech tagging, and text classification. PDF data extraction is gaining popularity across various industries, each using it to streamline processes and boost productivity. These industries include healthcare, customer service, academic research, spam filtering, recommendation systems, legal, and education. Challenges of extracting text from PDFs include document quality and size, domain-specific information, language variety, loss of semantic structure, and integration with multimodal frameworks. To mitigate these challenges, organizations can build a robust end-to-end pipeline to extract text from multiple PDF files using AI tools and techniques needed for smooth data extraction. Encord is an end-to-end AI-based multimodal data management and evaluation solution that allows users to develop scalable document processing pipelines for different applications, including text extraction from PDFs.