Looking for the right pipeline to convert academic textbook figures into interactive/editable assets [R]
Our take
The challenge presented by /u/Afraid_Reviewer highlights a critical, and often overlooked, area within the broader document understanding landscape: the transformation of static academic figures into dynamic, editable assets. This isn't simply about optical character recognition (OCR) or basic image processing; it’s about extracting structured data from visual representations that underpin scientific and educational knowledge. The desire to move beyond passively viewing textbook illustrations to actively manipulating them – repositioning labels, altering styles, and integrating them into modern workflows – speaks to a fundamental shift in how we interact with information. It also echoes concerns raised in "Structured AI data pipelines score 10.9 points below free-form code — DataFlow-Harness closes the gap," which underscores the value of well-defined data structures in AI applications, and why a robust pipeline for figure extraction is paramount. The need for a cost-effective solution further emphasizes the practical considerations for institutions and researchers working with large volumes of legacy academic content.
The core of the problem, as the user rightly identifies, lies in the delicate balance between automated detection and manual correction. Fully automated solutions, particularly those relying on large multimodal LLMs, can quickly become prohibitively expensive for this use case. A hybrid approach, leveraging AI for initial detection and segmentation while incorporating human review for refinement, is a sensible strategy. The questions raised – whether this is primarily a document layout analysis problem, image segmentation challenge, or something else – are all interconnected. It’s likely a combination, requiring a layered approach. Drawing inspiration from "At Waymo, an AI project isn't ready until its evals are — not when the model performs well," the focus should be on rigorous evaluation at each stage of the pipeline, not just on headline accuracy metrics. The human-in-the-loop element necessitates a robust evaluation framework to quantify the effectiveness of AI assistance and identify areas for improvement. The user's exploration of existing computer vision techniques, while yielding reasonable results for candidate region detection, points to the difficulty of label removal—a task that often requires nuanced understanding of the underlying illustration and its context.
The absence of readily available, pre-trained models specifically for textbook or scientific illustrations is a significant hurdle. While general-purpose image segmentation models can be adapted, their performance on this specialized domain is likely to be suboptimal. Exploring techniques like geometric heuristics, combined with lightweight machine learning models trained on a curated dataset of textbook figures, might offer a more cost-effective alternative to expensive LLMs. The potential for creating such a dataset, even a relatively small one, is a worthwhile investment. The search for open-source projects and relevant research is a crucial step, and the community's collective expertise could be instrumental in developing practical solutions. The challenge is not entirely novel; converting diagrams and illustrations to editable formats has been a longstanding goal in fields like technical documentation and CAD. However, the specific constraints of academic textbooks – the diversity of illustration styles, the presence of embedded labels, and the need for cost-effectiveness – present a unique set of challenges.
Ultimately, the success of this project hinges on finding the right balance between automation and human intervention, guided by a clear understanding of the cost-benefit trade-offs. It's a compelling reminder that transformative AI applications are rarely built on groundbreaking new algorithms alone; they often emerge from clever combinations of existing techniques, tailored to specific domain needs. As we continue to grapple with the explosion of digital information, the ability to convert static content into dynamic, interactive assets will become increasingly valuable. A key question moving forward is whether a community-driven effort, focused on creating specialized datasets and lightweight AI models, can unlock the potential of academic figures and empower a new generation of learners and researchers.
Hi everyone,
I'm working on a document understanding project and would appreciate some advice on the right technical direction.
The input will be scanned pages or images from academic books. I don't know in advance what kind of figures they'll contain—they could be biology diagrams, anatomy illustrations, chemistry figures, engineering drawings, maps, charts, art/history figures, or other educational illustrations.
My end goal is to convert these figures into a structured digital representation that can be controlled from the frontend.
The workflow I'm aiming for is:
- Upload a textbook page or image.
- Detect the figure(s) and their boundaries.
- Detect the labels/annotations that are already embedded in the figure (letters, numbers, arrows, callouts, etc.).
- Remove those existing labels while preserving the underlying illustration.
- Store the figure geometry (bounding boxes, polygons, masks, etc.) so my frontend can render its own labels that can be shown/hidden, translated, restyled, or repositioned.
This doesn't need to be fully automatic. In fact, the workflow will be human-assisted. If the AI detects a figure incorrectly, misses a region, or fails to remove a label cleanly, a human reviewer will correct it before it's finalized.
My priority is reducing manual work rather than eliminating it completely.
So far I've tried several computer vision approaches such as text detection, contour detection, line detection, and geometric heuristics. They work reasonably well for finding candidate regions, but the biggest challenge is cleaning the figures by removing the embedded labels while preserving the artwork underneath.
Another important requirement is cost. Since this could involve processing a large number of textbook pages, I'd like to avoid expensive multimodal LLMs or large vision models if there's a more traditional or lightweight pipeline that works well. I'm happy to use AI where it adds value, but I'd prefer a solution that keeps inference costs low.
Some questions I have:
- Is this primarily a document layout analysis problem, image segmentation, image inpainting, or something else?
- Are there models trained specifically for textbook or scientific illustrations rather than natural images?
- Is there a recommended low-cost pipeline for this kind of task?
- Has anyone built a human-in-the-loop workflow for document/figure annotation like this?
- Are there papers, datasets, or open-source projects that tackle converting textbook figures into editable, structured assets?
I'd really appreciate any suggestions, even if they're just pointers toward the right research area or open-source tools. Thanks!
[link] [comments]
Read on the original site
Open the publisher's page for the full experience