Handwritten Text Recognition: A Survey

2025-02-12

AI Summary

This survey addresses the robustness challenges in Handwritten Text Recognition (HTR) arising from high variability in handwriting styles, document layouts, and image quality. It systematically traces the evolution from heuristic approaches to end-to-end, document-level deep learning models. We propose the first unified analytical framework for HTR—comprehensively covering methodologies, benchmark evaluations, mainstream datasets, and performance comparisons—while explicitly defining recognition granularities: line-level and supra-line-level (i.e., paragraph- or document-level). The framework integrates CNNs, RNNs, Transformers, and attention mechanisms, synergizing sequence modeling strategies such as Connectionist Temporal Classification (CTC) and attention-based Seq2Seq to support multi-granularity recognition. Synthesizing over 100 works, we clarify the technical trajectory and identify core open challenges: cross-domain generalization, model interpretability, and system-level robustness. This work establishes a foundational theoretical and practical reference for developing real-world document understanding systems.

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Machine Learning: Evaluation and Analysis
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text

Abstract

Handwritten Text Recognition (HTR) has become an essential field within pattern recognition and machine learning, with applications spanning historical document preservation to modern data entry and accessibility solutions. The complexity of HTR lies in the high variability of handwriting, which makes it challenging to develop robust recognition systems. This survey examines the evolution of HTR models, tracing their progression from early heuristic-based approaches to contemporary state-of-the-art neural models, which leverage deep learning techniques. The scope of the field has also expanded, with models initially capable of recognizing only word-level content progressing to recent end-to-end document-level approaches. Our paper categorizes existing work into two primary levels of recognition: (1) emph{up to line-level}, encompassing word and line recognition, and (2) emph{beyond line-level}, addressing paragraph- and document-level challenges. We provide a unified framework that examines research methodologies, recent advances in benchmarking, key datasets in the field, and a discussion of the results reported in the literature. Finally, we identify pressing research challenges and outline promising future directions, aiming to equip researchers and practitioners with a roadmap for advancing the field.

Problem

• Challenges in robust handwritten text recognition
• Evolution from heuristic to deep learning models
• Expanding recognition from word to document level

Innovation

• Deep learning techniques
• End-to-end document-level approaches
• Unified research framework