|TREND||DATASET||BEST METHOD||PAPER TITLE||PAPER||CODE||COMPARE|
We present a neural network model - based on CNNs, RNNs and a novel attention mechanism - which achieves 84. 2% accuracy on the challenging French Street Name Signs (FSNS) dataset, significantly outperforming the previous state of the art (Smith'16), which achieved 72. 46%.
We describe efforts to adapt the Tesseract open source OCR engine for multiple scripts and languages.
We introduce the French Street Name Signs (FSNS) Dataset consisting of more than a million images of street name signs cropped from Google Street View images of France.
#3 best model for Optical Character Recognition on FSNS - Test
The goal of COCO-Text is to advance state-of-the-art in text detection and recognition in natural images.
We empirically demonstrate that the proposed approach achieves competitive performance on various challenging semantic segmentation benchmarks: Cityscapes, ADE20K, LIP, PASCAL-Context, and COCO-Stuff.
We present a neural encoder-decoder model to convert images into presentational markup based on a scalable coarse-to-fine attention mechanism.
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.
In contrast to most existing works that consist of multiple deep neural networks and several pre-processing steps we propose to use a single deep neural network that learns to detect and recognize text from natural images in a semi-supervised way.
#4 best model for Scene Text Detection on ICDAR 2013
An end-to-end trainable (fully differentiable) method for multi-language scene text localization and recognition is proposed.