[var.media_title;onformat=retitle]

Modern OCR uses machine learning techniques to train computers to read the text inside images. Specifically, deep Neural Networks (NN) are trained to analyze the text in images over many...

This paper proposes a novel approach for OCR using Convolutional Recurrent Neural Network (CRNN) that combines convolutional neural networks (CNNs) and recurrent neural networks (RNNs).

In this tutorial, you will learn how to train an Optical Character Recognition (OCR) model using Keras, TensorFlow, and Deep Learning.

Recently advancements in deep learning have greatly enhanced the OCR capabilities, primarily through the use of Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) 1, 6.

Optical Character Recognition (OCR) has significantly evolved with the rise of deep learning techniques. In this research paper, we present a novel and advanced OCR algorithm that leverages...

Optical Character Recognition (OCR) poses a crucial challenge within the realm of computer vision research, as it plays a pivotal role in converting vast amounts of unstructured text data into structured formats to support diverse artificial intelligence

基于深度学习OCR文本识别分为两个模块： DBNet 和 CRNN。 DBNet是基于分割的文本检测算法，算法将可微分二值化模块 (Differentiable Binarization)引入了分割模型，使得模型能够通过自适应的阈值图进行二值化，并且自适应阈值图可以计算损失，能够在模型训练过程中起到辅助效果优化的效果。 经过验证，该方案不仅提升了文本检测的效果而且简化了后处理过程。 相较于其他文本检测模型，DBNet在效果和性能上都有比较大的优势，是当前常用的文本检测算法。