Papers with Code 아카이브
Optical Character Recognition (OCR)
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Optical Character Recognition or Optical Character Reader (OCR) is the electronic or mechanical conversion of images of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene-photo (for example the text on signs and billboards in a landscape photo, license plates in cars...) or from subtitle text superimposed on an image (for example: from a television broadcast)
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/chreul/LAREX
공식 코드https://github.com/chreul/OCR_Testdata_EarlyPrintedBooks
공식 코드Transfer Learning for OCRopus Model Training on Early Printed Books
https://github.com/chreul/mptv
공식 코드Improving OCR Accuracy on Early Printed Books by combining Pretraining, Voting, and Active Learning
https://github.com/chris-lange/sdm_active_sampling
공식 코드https://github.com/chrisantuseze/active-self-pretraining
공식 코드A3: Active Adversarial Alignment for Source-Free Domain Adaptation
https://github.com/chritoth/active-bayesian-causal-inference
공식 코드https://github.com/chschroeder/self-training-for-sample-efficient-active-learning
공식 코드https://github.com/chufengt/APIS
공식 코드https://github.com/chungimungi/Generation-of-Colors-using-BiLSTMs
공식 코드Generation Of Colors using Bidirectional Long Short Term Memory Networks
https://github.com/chungimungi/color-prediction
공식 코드Generation Of Colors using Bidirectional Long Short Term Memory Networks
https://github.com/chungkwong/mathocr-myscript
공식 코드Stroke extraction for offline handwritten mathematical expression recognition
https://github.com/chungkwong/mathocr-myscript-android
공식 코드Stroke extraction for offline handwritten mathematical expression recognition
https://github.com/chungkwong/mathocr-tap
공식 코드Stroke extraction for offline handwritten mathematical expression recognition
https://github.com/cipri-tom/type-aware-crnn
공식 코드Field typing for improved recognition on heterogeneous handwritten forms
https://github.com/cjshui/WAAL
공식 코드Deep Active Learning: Unified and Principled Method for Query and Training
https://github.com/clinicalml/active_learn_to_defer
공식 코드Sample Efficient Learning of Predictors that Complement Humans
https://github.com/clovaai/CLEval
공식 코드CLEval: Character-Level Evaluation for Text Detection and Recognition Tasks
https://github.com/clovaai/bros
공식 코드https://github.com/clovaai/donut
공식 코드https://github.com/clovaai/webvicob
공식 코드On Web-based Visual Corpus Construction for Visual Document Understanding
https://github.com/codedecde/smal
공식 코드On Efficiently Acquiring Annotations for Multilingual Models
https://github.com/coleygroup/molpal
공식 코드Accelerating high-throughput virtual screening through molecular pool-based active learning
https://github.com/copenlu/intersectional-bias-pbw
공식 코드https://github.com/cosmic-cortex/modAL
공식 코드https://github.com/crispchris/IALE
공식 코드https://github.com/crusnic-corp/BN-DRISHTI
공식 코드https://github.com/cuent/ogb-kdd21
공식 코드On Graph Neural Network Ensembles for Large-Scale Molecular Property Prediction
https://github.com/curai/curai-research
공식 코드Adding more data does not always help: A study in medical conversation summarization with PEGASUS
https://github.com/cvc-dag/step
공식 코드https://github.com/cvjena/ITAL
공식 코드Information-Theoretic Active Learning for Content-Based Image Retrieval
https://github.com/cvlab-stonybrook/DocIIW
공식 코드https://github.com/cycloneboy/pdf_table
공식 코드PdfTable: A Unified Toolkit for Deep Learning-Based Table Extraction
https://github.com/cylindrical-penguin/Curiosity-Driven-RL-for-PFM
공식 코드Curiosity Driven Exploration to Optimize Structure-Property Learning in Microscopy
https://github.com/cyr19/syntaxchange
공식 코드Syntactic Language Change in English and German: Metrics, Parsers, and Convergences
https://github.com/daintlab/confidence-aware-learning
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.