Optimal Transport for Handwritten Text Recognition in a Low-Resource Regime
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911166283907072 |
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| author | Wraight, Petros Georgoulas Sfikas, Giorgos Kordonis, Ioannis Maragos, Petros Retsinas, George |
| author_facet | Wraight, Petros Georgoulas Sfikas, Giorgos Kordonis, Ioannis Maragos, Petros Retsinas, George |
| contents | Handwritten Text Recognition (HTR) is a task of central importance in the field of document image understanding. State-of-the-art methods for HTR require the use of extensive annotated sets for training, making them impractical for low-resource domains like historical archives or limited-size modern collections. This paper introduces a novel framework that, unlike the standard HTR model paradigm, can leverage mild prior knowledge of lexical characteristics; this is ideal for scenarios where labeled data are scarce. We propose an iterative bootstrapping approach that aligns visual features extracted from unlabeled images with semantic word representations using Optimal Transport (OT). Starting with a minimal set of labeled examples, the framework iteratively matches word images to text labels, generates pseudo-labels for high-confidence alignments, and retrains the recognizer on the growing dataset. Numerical experiments demonstrate that our iterative visual-semantic alignment scheme significantly improves recognition accuracy on low-resource HTR benchmarks. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_16977 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Optimal Transport for Handwritten Text Recognition in a Low-Resource Regime Wraight, Petros Georgoulas Sfikas, Giorgos Kordonis, Ioannis Maragos, Petros Retsinas, George Computer Vision and Pattern Recognition Machine Learning Handwritten Text Recognition (HTR) is a task of central importance in the field of document image understanding. State-of-the-art methods for HTR require the use of extensive annotated sets for training, making them impractical for low-resource domains like historical archives or limited-size modern collections. This paper introduces a novel framework that, unlike the standard HTR model paradigm, can leverage mild prior knowledge of lexical characteristics; this is ideal for scenarios where labeled data are scarce. We propose an iterative bootstrapping approach that aligns visual features extracted from unlabeled images with semantic word representations using Optimal Transport (OT). Starting with a minimal set of labeled examples, the framework iteratively matches word images to text labels, generates pseudo-labels for high-confidence alignments, and retrains the recognizer on the growing dataset. Numerical experiments demonstrate that our iterative visual-semantic alignment scheme significantly improves recognition accuracy on low-resource HTR benchmarks. |
| title | Optimal Transport for Handwritten Text Recognition in a Low-Resource Regime |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2509.16977 |