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LLM-1 Project Bootcamp: Computer Vision & Document AI

Science & Technology

Introduction

Welcome back to our session! Today marks the conclusion of our focus on Document AI and Computer Vision, where we have explored several key concepts and models. Following our discussion today, we will transition into Code Generation and then devote the remainder of the weeks to guiding you through your Capstone projects!

The objective today is for you to form small groups and propose a project that integrates a substantial portion of the skills and knowledge you have acquired so far. The project should be meaningful, realistic, and has real-world applicability. Ideally, it should be something you'd be proud to showcase on GitHub or discuss in your company or job interviews.

Recap of Document AI

Previously, we delved into Document AI through a hands-on approach, engaging in various exercises including Optical Character Recognition (OCR) and Visual Language Understanding (VLU). We observed the strengths and weaknesses of different models.

Today, we'll take a step back to trace the evolution of Document AI, a field that has seen significant advancements over the years. The traditional two-phase approach to OCR involved:

  1. Text Localization: Identifying bounding boxes around text.
  2. Text Understanding: Classifying content within those boxes.

While effective for simple text detection, this methodology often fails to grasp the comprehensive semantics of a document, which may include tables, figures, and various text elements conveying a singular message.

We’ll explore innovative approaches that treat Document AI as a visual language understanding challenge. Recent research has framed images in Document AI as a "foreign language," with the goal being to decode them into meaningful representations, similar to processing natural language.

Historical Perspective

Our exploration begins with a historical context from a notable paper published in 2014, when Convolutional Neural Networks (CNNs) were just gaining prominence. The paper demonstrated the potential of CNNs in object detection using innovative algorithms like RCNN, which significantly improved performance benchmarks.

Despite the evolution of techniques in the field, the challenge remains to improve the understanding of complex document structures. The introduction of visual transformers constituted a significant leap forward, allowing for images to be broken down into patches for processing akin to tokens in natural language models.

The DEIT (Data Efficient Image Transformers) paper built upon this foundation by allowing CNNs to act as teachers for transformer models. This introduced a new paradigm of learning that improved efficiency despite reduced access to data.

Key Contributions

The unique aspect of the DEIT approach is its use of knowledge distillation— where a student model learns from a more powerful teacher model to accelerate learning and improve performance. Distillation allows the student to align with teacher outputs while effectively minimizing the need for extensive datasets.

As we conclude this discussion about Document AI, it is important to reflect on how deeply intertwined these developments are with advancements in machine learning, coding, and algorithm efficiency.

Let us summarize today’s session and prepare for an engaging Capstone project session.

Keywords

Document AI, Computer Vision, Optical Character Recognition (OCR), Visual Language Understanding (VLU), Convolutional Neural Networks (CNNs), Deep Learning, Object Detection, Knowledge Distillation, Vision Transformers, Capstone Project.

FAQ

  • What will happen after today’s session? After today, we will begin Code Generation and then shift focus to your Capstone projects.

  • What is the aim of the Capstone project? The aim is to propose a project that leverages what you've learned in Document AI and Computer Vision and to create something useful that demonstrates your skills.

  • How does knowledge distillation work? Knowledge distillation involves a student model learning from a teacher model, effectively use the teacher's outputs to enhance its own learning efficiency and accuracy.

  • Where can we find the historical papers discussed? The historical papers, such as those related to RCNN and DEIT, can often be accessed through academic databases or websites like Google Scholar.

  • What should we aim for in our project proposals? Your project proposals should focus on being meaningful, realistic, and reflective of your learning, possibly something you can present in interviews or showcase on platforms like GitHub.

Thank you, and I’m looking forward to your innovative project ideas!