Papers with Code 아카이브
Handwritten Text Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 2
- 평가표
- 13
- 보존된 평가 기록
- 46
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.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Handwritten Text Recognition Using Convolutional Neural Network
Writer adaptation for offline text recognition: An exploration of neural network-based methods
Handwritten Text Recognition from Crowdsourced Annotations
BN-DRISHTI: Bangla Document Recognition through Instance-level Segmentation of Handwritten Text Images
How to Choose Pretrained Handwriting Recognition Models for Single Writer Fine-Tuning
Large Scale Genealogical Information Extraction From Handwritten Quebec Parish Records
SIMARA: a database for key-value information extraction from full pages
An end-to-end, interactive Deep Learning based Annotation system for cursive and print English handwritten text
Scalable handwritten text recognition system for lexicographic sources of under-resourced languages and alphabets
MSdocTr-Lite: A Lite Transformer for Full Page Multi-script Handwriting Recognition
CSSL-MHTR: Continual Self-Supervised Learning for Scalable Multi-script Handwritten Text Recognition
Uncovering the Handwritten Text in the Margins: End-to-end Handwritten Text Detection and Recognition
A Study of Augmentation Methods for Handwritten Stenography Recognition
The Learnable Typewriter: A Generative Approach to Text Analysis
Faster DAN: Multi-target Queries with Document Positional Encoding for End-to-end Handwritten Document Recognition
End-to-End Page-Level Assessment of Handwritten Text Recognition
Towards Robust Handwritten Text Recognition with On-the-fly User Participation
Enhancing Indic Handwritten Text Recognition Using Global Semantic Information
The Challenges of HTR Model Training: Feedback from the Project Donner le gout de l'archive a l'ere numerique
DARE: A large-scale handwritten date recognition system
Towards End-to-end Handwritten Document Recognition
A Few Shot Multi-Representation Approach for N-gram Spotting in Historical Manuscripts
Lexicon and Attention based Handwritten Text Recognition System
Boosting Modern and Historical Handwritten Text Recognition with Deformable Convolutions
The LAM Dataset: A Novel Benchmark for Line-Level Handwritten Text Recognition
2D Self-Organized ONN Model For Handwritten Text Recognition
A Lexicon and Depth-wise Separable Convolution Based Handwritten Text Recognition System
Self-Training of Handwritten Word Recognition for Synthetic-to-Real Adaptation
Easter2.0: Improving convolutional models for handwritten text recognition
BN-HTRd: A Benchmark Dataset for Document Level Offline Bangla Handwritten Text Recognition (HTR) and Line Segmentation
A Comprehensive Handwritten Paragraph Text Recognition System: LexiconNet
Content and Style Aware Generation of Text-line Images for Handwriting Recognition
DAN: a Segmentation-free Document Attention Network for Handwritten Document Recognition
Improving Amharic Handwritten Word Recognition Using Auxiliary Task
AttentionHTR: Handwritten Text Recognition Based on Attention Encoder-Decoder Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.