Optical Character Recognition (OCR)
314 papers with code • 5 benchmarks • 42 datasets
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)
Benchmarks
These leaderboards are used to track progress in Optical Character Recognition (OCR)
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Libraries
Use these libraries to find Optical Character Recognition (OCR) models and implementations
IAM
FUNSD
ST-VQA
TextCaps
ICDAR 2003
TextOCR
UFPR-ALPR
im2latex-100k
Subtasks
Most implemented papers
ASTER: An Attentional Scene Text Recognizer with Flexible Rectification
SCENE text recognition has attracted great interest from the academia and the industry in recent years owing to its importance in a wide range of applications.
Stroke extraction for offline handwritten mathematical expression recognition
Given a ready-made state-of-the-art online handwritten mathematical expression recognizer, the proposed procedure correctly recognized 58. 22%, 65. 65%, and 65. 22% of the offline formulas rendered from the datasets of the Competitions on Recognition of Online Handwritten Mathematical Expressions(CROHME) in 2014, 2016, and 2019 respectively.
FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents
We present a new dataset for form understanding in noisy scanned documents (FUNSD) that aims at extracting and structuring the textual content of forms.
Multimodal deep networks for text and image-based document classification
Classification of document images is a critical step for archival of old manuscripts, online subscription and administrative procedures.
ScrabbleGAN: Semi-Supervised Varying Length Handwritten Text Generation
amzn/convolutional-handwriting-gan
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CVPR 2020
This is especially true for handwritten text recognition (HTR), where each author has a unique style, unlike printed text, where the variation is smaller by design.
Fully Unsupervised Diversity Denoising with Convolutional Variational Autoencoders
Deep Learning based methods have emerged as the indisputable leaders for virtually all image restoration tasks.
PP-OCRv2: Bag of Tricks for Ultra Lightweight OCR System
Optical Character Recognition (OCR) systems have been widely used in various of application scenarios.
DocScanner: Robust Document Image Rectification with Progressive Learning
The iterative refinements make DocScanner converge to a robust and superior rectification performance, while the lightweight recurrent architecture ensures the running efficiency.
DiT: Self-supervised Pre-training for Document Image Transformer
microsoft/unilm
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We leverage DiT as the backbone network in a variety of vision-based Document AI tasks, including document image classification, document layout analysis, table detection as well as text detection for OCR.
Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification
ihsaan-ullah/meta-album
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NeurIPS 2022
We introduce Meta-Album, an image classification meta-dataset designed to facilitate few-shot learning, transfer learning, meta-learning, among other tasks.
