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Handwritten Text Recognition
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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.
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Learning to Align: Addressing Character Frequency Distribution Shifts in Handwritten Text Recognition
MetaWriter: Personalized Handwritten Text Recognition Using Meta-Learned Prompt Tuning
Preserving Privacy Without Compromising Accuracy: Machine Unlearning for Handwritten Text Recognition
Meta-DAN: towards an efficient prediction strategy for page-level handwritten text recognition
TRIDIS: A Comprehensive Medieval and Early Modern Corpus for HTR and NER
Benchmarking Large Language Models for Handwritten Text Recognition
Judge a Book by its Cover: Investigating Multi-Modal LLMs for Multi-Page Handwritten Document Transcription
Handwritten Text Recognition: A Survey
Col-OLHTR: A Novel Framework for Multimodal Online Handwritten Text Recognition
DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning
HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis
HTR-JAND: Handwritten Text Recognition with Joint Attention Network and Knowledge Distillation
On the Generalization of Handwritten Text Recognition Models
Nuremberg Letterbooks: A Multi-Transcriptional Dataset of Early 15th Century Manuscripts for Document Analysis
Unlocking the Archives: Using Large Language Models to Transcribe Handwritten Historical Documents
Integrating Canonical Neural Units and Multi-Scale Training for Handwritten Text Recognition
Hespi: A pipeline for automatically detecting information from hebarium specimen sheets
HATFormer: Historic Handwritten Arabic Text Recognition with Transformers
HTR-VT: Handwritten Text Recognition with Vision Transformer
Platypus: A Generalized Specialist Model for Reading Text in Various Forms
DANIEL: A fast Document Attention Network for Information Extraction and Labelling of handwritten documents
Muharaf: Manuscripts of Handwritten Arabic Dataset for Cursive Text Recognition
Arabic Handwritten Text for Person Biometric Identification: A Deep Learning Approach
Revisiting N-Gram Models: Their Impact in Modern Neural Networks for Handwritten Text Recognition
End-to-end information extraction in handwritten documents: Understanding Paris marriage records from 1880 to 1940
Align, Minimize and Diversify: A Source-Free Unsupervised Domain Adaptation Method for Handwritten Text Recognition
GatedLexiconNet: A Comprehensive End-to-End Handwritten Paragraph Text Recognition System
Best Practices for a Handwritten Text Recognition System
Spatial Context-based Self-Supervised Learning for Handwritten Text Recognition
Data Generation for Post-OCR correction of Cyrillic handwriting
Exploring OCR Capabilities of GPT-4V(ision) : A Quantitative and In-depth Evaluation
DTrOCR: Decoder-only Transformer for Optical Character Recognition
A tailored Handwritten-Text-Recognition System for Medieval Latin
Handwritten Stenography Recognition and the LION Dataset
ReLU and Addition-based Gated RNN
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