SOTAVerified

Handwritten Text Recognition

Handwritten Text Recognition (HTR) is the task of automatically identifying and transcribing handwritten text from images or scanned documents into machine-readable text. The goal is to develop a system capable of accurately interpreting diverse handwriting styles, accounting for variations in alignment, stroke, spacing, and noise. This task involves detecting handwritten regions within an image, extracting the text content, and converting it into a structured digital format, enabling further search, indexing, or data analysis.

Papers

Showing 1–10 of 139 papers

TitleStatusHype
Advancing Offline Handwritten Text Recognition: A Systematic Review of Data Augmentation and Generation Techniques—0
Learning to Align: Addressing Character Frequency Distribution Shifts in Handwritten Text RecognitionCode0
MetaWriter: Personalized Handwritten Text Recognition Using Meta-Learned Prompt Tuning—0
Preserving Privacy Without Compromising Accuracy: Machine Unlearning for Handwritten Text Recognition—0
Meta-DAN: towards an efficient prediction strategy for page-level handwritten text recognitionCode1
TRIDIS: A Comprehensive Medieval and Early Modern Corpus for HTR and NER—0
Benchmarking Large Language Models for Handwritten Text Recognition—0
Judge a Book by its Cover: Investigating Multi-Modal LLMs for Multi-Page Handwritten Document TranscriptionCode0
Handwritten Text Recognition: A Survey—0
Col-OLHTR: A Novel Framework for Multimodal Online Handwritten Text Recognition—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1StackMix+BlotsCER2.5—Unverified