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/ofa-sys/ofa
공식 코드Transferring General Multimodal Pretrained Models to Text Recognition
https://github.com/olivesgatech/RoSE
공식 코드Targeting Negative Flips in Active Learning using Validation Sets
https://github.com/olivesgatech/clinical-trial-active-learning
공식 코드https://github.com/olivesgatech/focal_dataset
공식 코드https://github.com/olivesgatech/gauss
공식 코드Gaussian Switch Sampling: A Second Order Approach to Active Learning
https://github.com/olivesgatech/introspective-learning
공식 코드Introspective Learning : A Two-Stage Approach for Inference in Neural Networks
https://github.com/olivesgatech/man-recon
공식 코드https://github.com/omegafragger/DDU
공식 코드https://github.com/open-mmlab/mmocr
공식 코드MMOCR: A Comprehensive Toolbox for Text Detection, Recognition and Understanding
https://github.com/open-mmlab/mmocr/blob/main/configs/textrecog/robust_scanner/README.md
공식 코드RobustScanner: Dynamically Enhancing Positional Clues for Robust Text Recognition
https://github.com/openai/moderation-api-release
공식 코드A Holistic Approach to Undesired Content Detection in the Real World
https://github.com/openai/prm800k
공식 코드https://github.com/opendatalab/OHR-Bench
공식 코드OCR Hinders RAG: Evaluating the Cascading Impact of OCR on Retrieval-Augmented Generation
https://github.com/opendatalab/PDF-Extract-Kit
공식 코드MinerU: An Open-Source Solution for Precise Document Content Extraction
https://github.com/opendatalab/mineru
공식 코드MinerU: An Open-Source Solution for Precise Document Content Extraction
https://github.com/opendatalab/pm4bench
공식 코드PM4Bench: A Parallel Multilingual Multi-Modal Multi-task Benchmark for Large Vision Language Model
https://github.com/opengvlab/internvl
공식 코드How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites
https://github.com/openmatch/activerag
공식 코드ActiveRAG: Autonomously Knowledge Assimilation and Accommodation through Retrieval-Augmented Agents
https://github.com/ortonwang/pcdal
공식 코드https://github.com/osimeoni/RethinkingDeepActiveLearning
공식 코드Rethinking deep active learning: Using unlabeled data at model training
https://github.com/ozansener/active_learning_coreset
공식 코드Active Learning for Convolutional Neural Networks: A Core-Set Approach
https://github.com/paarandika/Gradient-Approx-to-improve-OCR
공식 코드Unknown-box Approximation to Improve Optical Character Recognition Performance
https://github.com/park-jay/slavery-discourse
공식 코드https://github.com/parmoo/cfr-materials
공식 코드https://github.com/pathak22/exploration-by-disagreement
공식 코드https://github.com/patrickzh/deepcore
공식 코드DeepCore: A Comprehensive Library for Coreset Selection in Deep Learning
https://github.com/peiyunh/alpf
공식 코드https://github.com/peluz/victor-visual-text
공식 코드Sequence-aware multimodal page classification of Brazilian legal documents
https://github.com/pgiouroukis/dual
공식 코드DUAL: Diversity and Uncertainty Active Learning for Text Summarization
https://github.com/philip-schrodt/ngec
공식 코드Creating Custom Event Data Without Dictionaries: A Bag-of-Tricks
https://github.com/philipp-kohl/active-learning-evaluation-framework
공식 코드https://github.com/philipp-kohl/scoping-review-active-learning-er
공식 코드https://github.com/pieter-jacobs/bachelor-thesis
공식 코드Active learning for reducing labeling effort in text classification tasks
https://github.com/pieterblok/boxal
공식 코드Improved detection of discarded fish species through BoxAL active learning
https://github.com/pieterblok/maskal
공식 코드Active learning with MaskAL reduces annotation effort for training Mask R-CNN
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.