Papers with Code 아카이브

Optical Character Recognition (OCR)

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보존된 평가 기록: 20

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/ikigai-aa/Automatic-License-Plate-Recognition

A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector

https://github.com/impira/docquery

OCR-free Document Understanding Transformer

https://github.com/infly-ai/inf-mllm

Infinity Parser: Layout Aware Reinforcement Learning for Scanned Document Parsing

https://github.com/ingbeeedd/EAST-tf2keras

EAST: An Efficient and Accurate Scene Text Detector

https://github.com/intel/handwritten-chinese-ocr-samples

Writer-Aware CNN for Parsimonious HMM-Based Offline Handwritten Chinese Text Recognition

https://github.com/isaacaddis/Seung

EAST: An Efficient and Accurate Scene Text Detector

https://github.com/isayev/ANI1_dataset

Less is more: sampling chemical space with active learning

https://github.com/ispamm/img2img-sc

NAF-DPM: A Nonlinear Activation-Free Diffusion Probabilistic Model for Document Enhancement

https://github.com/iwyoo/tf_thinplatespline

Robust Scene Text Recognition with Automatic Rectification

https://github.com/jackknife007/crnn

An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition

https://github.com/jaesik817/bmaml

Bayesian Model-Agnostic Meta-Learning

https://github.com/jaesik817/bmaml_rl

Bayesian Model-Agnostic Meta-Learning

https://github.com/jakeywu/ocr_torch

Real-time Scene Text Detection with Differentiable Binarization

https://github.com/jamestjw/OCR

Evaluating Sequence-to-Sequence Models for Handwritten Text Recognition

https://github.com/jarl93/joeynmt-modified

Self-Regulated Interactive Sequence-to-Sequence Learning

https://github.com/javiferran/document-classification

Improving accuracy and speeding up Document Image Classification through parallel systems

https://github.com/jeasung-pf/MORAN_v2

A Multi-Object Rectified Attention Network for Scene Text Recognition

https://github.com/jeasung-pf/see

SEE: Towards Semi-Supervised End-to-End Scene Text Recognition

https://github.com/jeremyrchow/text-generation-kaggle

Speech Recognition with Deep Recurrent Neural Networks

https://github.com/jingxixu/Kitchen2D---stir

Active model learning and diverse action sampling for task and motion planning

https://github.com/jnkl314/tin_dr

EAST: An Efficient and Accurate Scene Text Detector

https://github.com/joanrod/figure-diffusion

OCR-VQGAN: Taming Text-within-Image Generation

https://github.com/johntiger1/csc2547-project

Variational Adversarial Active Learning

https://github.com/johntiger1/vaal

Variational Adversarial Active Learning

https://github.com/johntiger1/vaal_querying

Variational Adversarial Active Learning

https://github.com/jonhue/transductive-active-learning

Transductive Active Learning: Theory and Applications

https://github.com/jsalbert/biotorch

How Important is Weight Symmetry in Backpropagation?

https://github.com/juanluisrosaramos/CRNN_OCR

An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition

https://github.com/juglab/DivNoising

Fully Unsupervised Diversity Denoising with Convolutional Variational Autoencoders

https://github.com/julesberman/colora

Neural Galerkin Schemes with Active Learning for High-Dimensional Evolution Equations

https://github.com/junstar92/hangul-syllable-recognition

An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition

https://github.com/junwang23/deepdirtycodes

A Critical Review of Recurrent Neural Networks for Sequence Learning

https://github.com/justinessert/hierarchical-deep-cnn

Spatially-sparse convolutional neural networks

https://github.com/justld/contrast_seg_paddle

Exploring Cross-Image Pixel Contrast for Semantic Segmentation

https://github.com/jzbjyb/rri_match

Multi-Dimensional Recurrent Neural Networks

https://github.com/kahnchana/RNN

Speech Recognition with Deep Recurrent Neural Networks

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.