Representing Online Handwriting for Recognition in Large Vision-Language Models
This work addresses the need for accurate handwriting-to-text conversion in tablets and touchscreens, enabling search, indexing, and AI assistance, with an incremental improvement over existing methods.
The paper tackles the problem of poor handwriting recognition in vision-language models (VLMs) by proposing a novel tokenized representation of digital ink that includes time-ordered strokes as both text and image, achieving results comparable to or better than state-of-the-art online handwriting recognizers on multiple public datasets.
The adoption of tablets with touchscreens and styluses is increasing, and a key feature is converting handwriting to text, enabling search, indexing, and AI assistance. Meanwhile, vision-language models (VLMs) are now the go-to solution for image understanding, thanks to both their state-of-the-art performance across a variety of tasks and the simplicity of a unified approach to training, fine-tuning, and inference. While VLMs obtain high performance on image-based tasks, they perform poorly on handwriting recognition when applied naively, i.e., by rendering handwriting as an image and performing optical character recognition (OCR). In this paper, we study online handwriting recognition with VLMs, going beyond naive OCR. We propose a novel tokenized representation of digital ink (online handwriting) that includes both a time-ordered sequence of strokes as text, and as image. We show that this representation yields results comparable to or better than state-of-the-art online handwriting recognizers. Wide applicability is shown through results with two different VLM families, on multiple public datasets. Our approach can be applied to off-the-shelf VLMs, does not require any changes in their architecture, and can be used in both fine-tuning and parameter-efficient tuning. We perform a detailed ablation study to identify the key elements of the proposed representation.