Human Activity Recognition using TensorFlow (CNN + LSTM)

推荐开源项目：基于TensorFlow的CNN_LSTM_CTC OCR识别系统在当前深度学习的浪潮中，光学字符识别（OCR）技术因其在自动化文档处理、图像文字提取等领域的广泛应用而备受关注。

This collection demonstrates how to construct and train a deep, bidirectional stacked LSTM using CNN features as input with CTC loss to perform robust word recognition.

This project implements a complete OCR system that recognizes handwritten English alphabet letters (A–Z) using a Convolutional Neural Network (CNN) built from scratch with TensorFlow.

This article explores the powerful capabilities of OCR and presents a TensorFlow-based model, a testament to the evolution of deep learning in unlocking the potential of visual data.

This tutorial demonstrates training a simple Convolutional Neural Network (CNN) to classify CIFAR images. Because this tutorial uses the Keras Sequential API, creating and training your model will take just a few lines of code.

從資料預處理到模型訓練評估，一步步瞭解如何使用TensorFlow訓練自製OCR模型，辨識驗證碼。

OCR （Optical Character Recognition， 光学字符识别），其目的很简单，只是要把影像作一个转换，使影像内的图形继续保存、有表格则表格内资料及影像内的文字，一律变成计算机文字，使能达到影像资料的储存量减少、识别出的文字可再使用及分析，这样可节省人力 ...