Nabin Khanal

Nabin Khanal, Tongyan Wang, Jui-Cheng Chiu et al.

Digitizing complex documents with handwritten content, irregular tables, and heterogeneous layouts remains challenging, as traditional Optical Character Recognition (OCR) systems fail to capture writing nuances, author-specific conventions, and document structure, and recent LLM-based approaches lack mechanisms for precise, scalable correction. We present an interactive document digitization system that integrates layout-aware parsing, OCR, and LLM-based reconstruction with user-driven refinement. The system is informed by a formative study that identifies key challenges and interaction needs in real-world digitization workflows. It supports both direct edits and natural-language instructions, and introduces a layout-aware propagation mechanism that generalizes user corrections across structurally similar regions. This enables not only efficient error correction but also document re-shaping into structured, analyzable representations. We evaluate the system through a within-subjects user study (n=12) on real-world documents. Results show improved correction efficiency and reduced repetitive effort, demonstrating more effective and controllable document digitization procedure.

Nabin Khanal, Chun Meng Yu, Jui-Cheng Chiu et al.

We introduce FathomGPT, an open source system for the interactive investigation of ocean science data via a natural language interface. FathomGPT was developed in close collaboration with marine scientists to enable researchers to explore and analyze the FathomNet image database. FathomGPT provides a custom information retrieval pipeline that leverages OpenAI's large language models to enable: the creation of complex queries to retrieve images, taxonomic information, and scientific measurements; mapping common names and morphological features to scientific names; generating interactive charts on demand; and searching by image or specified patterns within an image. In designing FathomGPT, particular emphasis was placed on enhancing the user's experience by facilitating free-form exploration and optimizing response times. We present an architectural overview and implementation details of FathomGPT, along with a series of ablation studies that demonstrate the effectiveness of our approach to name resolution, fine tuning, and prompt modification. We also present usage scenarios of interactive data exploration sessions and document feedback from ocean scientists and machine learning experts.