Displaying 1 to 7 from 7 results

pixel_level_land_classification - Tutorial demonstrating how to create a semantic segmentation (pixel-level classification) model to predict land cover from aerial imagery

  •    Jupyter

This repository contains a tutorial illustrating how to create a deep neural network model that accepts an aerial image as input and returns a land cover label (forested, water, etc.) for every pixel in the image. Microsoft's Cognitive Toolkit (CNTK) is used to train and evaluate the model on an Azure Geo AI Data Science Virtual Machine or an Azure Batch AI GPU cluster. The method shown here was developed in collaboration between the Chesapeake Conservancy, ESRI, and Microsoft Research as part of Microsoft's AI for Earth initiative. We recommend budgeting two hours for a full walkthrough of this tutorial. The code, shell commands, trained models, and sample images provided here may prove helpful even if you prefer not to complete the walkthrough: we have provided explanations and direct links to these materials where possible.

translator - Go client for Microsoft Text Translation API and Google Cloud Translation API

  •    Go

Go package for easy access to Microsoft Text Translation API and Google Translate API. Register for Microsoft Text Translation API (see instructions). Use the obtained subscription key to instantiate a translator as shown below.

handprint - Apply different text recognition services to images of handwritten documents.

  •    Python

A Python program to apply different handwritten text recognition services to images of handwritten text pages, and produce an annotated image (and optionally more) showing the text recognized. Version 1.0.1: This version adds instructions for installing from PyPI and fixes a bug writing files downloaded from URLs.

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