InkFM: A Foundational Model for Full-Page Online Handwritten Note Understanding

• URL: http://arxiv.org/abs/2503.23081v1
• Date: Sat, 29 Mar 2025 13:45:24 GMT
• Title: InkFM: A Foundational Model for Full-Page Online Handwritten Note Understanding
• Authors: Anastasiia Fadeeva, Vincent Coriou, Diego Antognini, Claudiu Musat, Andrii Maksai,
• Abstract summary: We introduce a foundational model called InkFM for analyzing full pages of handwritten content.<n>It offers a unique combination of capabilities: recognizing text in 28 different scripts, mathematical expressions recognition, and segmenting pages into distinct elements like text and drawings.
• Score: 10.065311465170382
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tablets and styluses are increasingly popular for taking notes. To optimize this experience and ensure a smooth and efficient workflow, it's important to develop methods for accurately interpreting and understanding the content of handwritten digital notes. We introduce a foundational model called InkFM for analyzing full pages of handwritten content. Trained on a diverse mixture of tasks, this model offers a unique combination of capabilities: recognizing text in 28 different scripts, mathematical expressions recognition, and segmenting pages into distinct elements like text and drawings. Our results demonstrate that these tasks can be effectively unified within a single model, achieving SoTA text line segmentation out-of-the-box quality surpassing public baselines like docTR. Fine- or LoRA-tuning our base model on public datasets further improves the quality of page segmentation, achieves state-of the art text recognition (DeepWriting, CASIA, SCUT, and Mathwriting datasets) and sketch classification (QuickDraw). This adaptability of InkFM provides a powerful starting point for developing applications with handwritten input.

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