HTR-ConvText: Leveraging Convolution and Textual Information for Handwritten Text Recognition

2025-12-04

AI Summary

Handwritten Text Recognition (HTR) faces three key challenges: data scarcity, high variability in handwriting styles, and difficulty modeling complex diacritical marks—limiting generalization and increasing reliance on synthetic data. To address these, we propose HTR-ConvText: a novel architecture featuring a ConvText hybrid encoder that jointly captures local convolutional features and global textual context, substantially compressing sequence length and improving inference efficiency. We introduce a text-auxiliary module to enhance Connectionist Temporal Classification (CTC) decoding, reducing dependency on synthetic training data. The backbone integrates position-encoded MobileViT with residual CNNs in a hierarchical design, augmented by a text-context injection mechanism. Evaluated on IAM, READ2016, LAM, and HANDS-VNOnDB, HTR-ConvText achieves state-of-the-art performance under low-data and high-style-diversity settings, demonstrating superior generalization and robustness across diverse handwriting domains.

Technology Category

• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Search and Optimization: Non-convex Optimization
• Machine Learning: Learning with Manifolds

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Handwritten Text Recognition remains challenging due to the limited data, high writing style variance, and scripts with complex diacritics. Existing approaches, though partially address these issues, often struggle to generalize without massive synthetic data. To address these challenges, we propose HTR-ConvText, a model designed to capture fine-grained, stroke-level local features while preserving global contextual dependencies. In the feature extraction stage, we integrate a residual Convolutional Neural Network backbone with a MobileViT with Positional Encoding block. This enables the model to both capture structural patterns and learn subtle writing details. We then introduce the ConvText encoder, a hybrid architecture combining global context and local features within a hierarchical structure that reduces sequence length for improved efficiency. Additionally, an auxiliary module injects textual context to mitigate the weakness of Connectionist Temporal Classification. Evaluations on IAM, READ2016, LAM and HANDS-VNOnDB demonstrate that our approach achieves improved performance and better generalization compared to existing methods, especially in scenarios with limited training samples and high handwriting diversity.

Problem

• Recognizes handwritten text with limited data and diverse styles
• Captures both local stroke features and global contextual dependencies
• Improves generalization in low-data and high-diversity scenarios

Innovation

• Residual CNN with MobileViT for feature extraction
• Hybrid ConvText encoder reduces sequence length
• Auxiliary module injects textual context to CTC