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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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Canjie-Luo/Scene-Text-Image-Transformer
공식 코드https://github.com/FactoDeepLearning/LinePytorchOCR
공식 코드Recurrence-free unconstrained handwritten text recognition using gated fully convolutional network
https://github.com/FactoDeepLearning/SPAN
공식 코드SPAN: a Simple Predict & Align Network for Handwritten Paragraph Recognition
https://github.com/FactoDeepLearning/VerticalAttentionOCR
공식 코드End-to-end Handwritten Paragraph Text Recognition Using a Vertical Attention Network
https://github.com/IntuitionMachines/OrigamiNet
공식 코드https://github.com/Leedeng/Line-Counter
공식 코드LineCounter: Learning Handwritten Text Line Segmentation by Counting
https://github.com/MarkPotanin/DigitalPeter
공식 코드Digital Peter: Dataset, Competition and Handwriting Recognition Methods
https://github.com/MattAlexMiracle/SmartPatch
공식 코드SmartPatch: Improving Handwritten Word Imitation with Patch Discriminators
https://github.com/Pay20Y/SEED
공식 코드Enhancing Indic Handwritten Text Recognition Using Global Semantic Information
https://github.com/Wang-Tianwei/Decoupled-attention-network
공식 코드https://github.com/abdoelsayed2016/HKR_Dataset
공식 코드https://github.com/abdoelsayed2016/KOHTD
공식 코드https://github.com/adamaxelsson/project-marginalia
공식 코드https://github.com/alibabaresearch/advancedliteratemachinery
공식 코드Platypus: A Generalized Specialist Model for Reading Text in Various Forms
https://github.com/amazon-research/textadain-robust-recognition
공식 코드TextAdaIN: Paying Attention to Shortcut Learning in Text Recognizers
https://github.com/crusnic-corp/BN-DRISHTI
공식 코드https://github.com/dali92002/htrbymatching
공식 코드https://github.com/dbrainio/cyrillichandwritingpoc
공식 코드Data Generation for Post-OCR correction of Cyrillic handwriting
https://github.com/dmitrijsk/attentionhtr
공식 코드AttentionHTR: Handwritten Text Recognition Based on Attention Encoder-Decoder Networks
https://github.com/ektavats/project-marginalia
공식 코드https://github.com/factodeeplearning/dan
공식 코드DAN: a Segmentation-free Document Attention Network for Handwritten Document Recognition
https://github.com/factodeeplearning/fasterdan
공식 코드https://github.com/factodeeplearning/meta_dan
공식 코드Meta-DAN: towards an efficient prediction strategy for page-level handwritten text recognition
https://github.com/georgeretsi/htr-best-practices
공식 코드https://github.com/jvpoulos/Attention-OCR
공식 코드Character-Based Handwritten Text Transcription with Attention Networks
https://github.com/kartikgill/easter2
공식 코드Easter2.0: Improving convolutional models for handwritten text recognition
https://github.com/mauvilsa/imgtxtenh
공식 코드Continuous Offline Handwriting Recognition using Deep Learning Models
https://github.com/mehreenmehreen/muharaf
공식 코드Muharaf: Manuscripts of Handwritten Arabic Dataset for Cursive Text Recognition
https://github.com/mehreenmehreen/start_follow_read_arabic
공식 코드Muharaf: Manuscripts of Handwritten Arabic Dataset for Cursive Text Recognition
https://github.com/mhumphries2323/Transcription_Pearl
공식 코드Unlocking the Archives: Using Large Language Models to Transcribe Handwritten Historical Documents
https://github.com/microsoft/unilm/tree/master/trocr
공식 코드TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models
https://github.com/planet-ai-gmbh/tfaip-hybrid-ctc-s2s
공식 코드Rescoring Sequence-to-Sequence Models for Text Line Recognition with CTC-Prefixes
https://github.com/prhlt/e2ehtreval
공식 코드End-to-End Page-Level Assessment of Handwritten Text Recognition
https://github.com/pstroe/atr-eval
공식 코드https://github.com/rbturnbull/hespi
공식 코드Hespi: A pipeline for automatically detecting information from hebarium specimen sheets
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