AI Content Describer for NVDA

This add-on makes it possible to obtain detailed descriptions for images, user interface controls, and other visually inaccessible content.

Leveraging the multimodal capabilities of advanced AI models and computer vision algorithms, we aim to deliver best-in-class content descriptions and increase independence overall. For more information about the underlying models, refer to the corresponding section of this document.

Features

Use case

There were a few primary motivations behind this project.

NVDA is capable of performing optical character recognition (OCR) out of the box, which is a game changer. If you are trying to get text out of an image or PDF document, this is what you're looking for.

However, OCR is only able to analyze data that might be text. It falls short at considering the context, objects and relationships conveyed in those images. And the internet is full of them. Logos, portraits, memes, icons, charts, diagrams, bar/line graphs... You name it. They're everywhere, and usually not in a format that screen reader users can interpret. Until recently, there has been an unwavering reliance on content authors providing alternative text descriptions. While this is still a must, it's difficult to change the fact that a high standard of quality happens to be the exception, not the rule.

Now, the possibilities are almost endless. You might:

Models

Follow the instructions provided below to get each of these working.

Getting started

Download the latest release of the add-on from this link. Click on the file on a computer with NVDA installed, then follow the instructions below to obtain an API key from a supported provider. If you are unsure about which one to use, the consensus of this addon's developer and testers is that Gemini currently offers more reasonable pricing, while OpenAI seems to provide a higher degree of accuracy. Claude 3 haiku is the cheapest and fastest option but the quality is hit or miss. Of course, these results are highly dependent on the task at hand, so we recommend experimenting with different models and prompts to find what works best.

Obtaining an API key from OpenAI:

  1. Go to https://platform.openai.com/account/api-keys
  2. If you don't yet have an account, create one. If you do, log in.
  3. On the API keys page, click to create a new secret key. Copy it to your clipboard.
  4. Fund the account with at least $1
  5. In the NVDA settings dialog, scroll down to the AI Content Describer category, then choose "manage models (alt+m)", select "GPT4 Vision" as the provider, tab into the API key field, and paste the key you just generated here.

At the time of this writing, OpenAI issues credits to new developer accounts that can be used for three months, after which they are lost. Following this period, you will have to purchase credits. Typical usage shouldn't ever exceed $5.00 per month. For a point of reference, the original version of this add-on was developed for slightly under a dollar. It is always possible to login to your OpenAI account and click on "usage" to get your quota.

Obtaining an API key from Google

  1. You will first need to create a Google workspace project by following this link. Make sure you are logged in to your account. https://console.cloud.google.com/projectcreate
  2. Create a name between four and thirty characters, like "gemini" or "NVDA add-on"
  3. Navigate to this URL: https://makersuite.google.com/app/apikey
  4. Click "create API key"
  5. In the NVDA settings dialog, scroll down to the AI Content Describer category, then choose "manage models (alt+m)", select "Google Gemini" as your provider, tab into the API key field, and paste the key you just generated here.

Obtaining an API key from Anthropic

  1. Login to the Anthropic console.
  2. Click on your profile -> API keys.
  3. Click Create Key.
  4. Enter a name for the key, like "AIContentDescriber", then click on "Create Key" and copy the value that shows up. This is what you will paste into the API key field under the Ai Content Describer category of the NVDA settings dialog -> manage models -> Claude 3.
  5. If you haven't already, purchase at least $5 in credits under the plans page at https://console.anthropic.com/settings/plans.

Setting up llama.cpp

This provider is currently somewhat buggy, and your mileage may very. It should really only be attempted by advanced users with an interest in running local self-hosted models, and the hardware to do so.

  1. Download llama.cpp. At the time of this writing, this pull request removes multimodal capabilities so you will want to use the last version with support for this. If you are running on an Nvidia graphics adapter with CUDA support, download these prebuilt binaries: llama-b2356-bin-win-cublas-cu12.2.0-x64.zip and cudart-llama-bin-win-cu12.2.0-x64.zip The steps for working with a different graphics adapter are out of scope, but can be found in the llama.cpp readme.
  2. Extract both of these files into the same folder.
  3. Locate the quantized formats of the models you'd like to use from Huggingface. For LLaVA 1.6 Vicuna 7B: llava-v1.6-vicuna-7b.Q4_K_M.gguf and mmproj-model-f16.gguf
  4. Put these files in the folder with the rest of the llama.cpp binaries.
  5. From a command prompt, run the llava.cpp server binary, passing the .gguf files for the model and multimodal projector (as follows): server.exe -m llava-v1.6-vicuna-7b.Q4_K_M.gguf --mmproj mmproj-model-f16.gguf
  6. In the NVDA settings dialog, scroll down to the AI Content Describer category, then choose "manage models (alt+m)", select "llama.cpp" as your provider, tab into the base URL field, and enter the endpoint shown in the console (defaults to "http://localhost:8080").
  7. Alternatively, you may omit some of these steps and run llama.cpp on a remote server with higher specs than your local machine, then enter that endpoint instead.

Usage

Four hotkeys are bound by default:

Three gestures are unbound:

Don't hesitate to customize these at any time from the input gestures dialog.

Building the add-on

To create the add-on package from source, you will need:

Then open your terminal of choice:

git clone https://github.com/cartertemm/AI-content-describer.git cd AI-content-describer scons

After the scons command is finished executing, a *.nvda-addon file will be placed in the root of this repository ready for testing and release.

If you add additional strings that need to be translated, it is important to rebuild the .pot file like so:

scons pot

How to translate?

On a windows machine:

Contributions

All are highly appreciated and will be credited. The following people have worked on the addon.

Facing a problem? Submit it to the issue tracker

Have a suggestion for a new feature? Create a ticket for that as well, and we can talk about implementing it. Pull requests without associated issues will be reviewed, but are likely to take up more time for everyone, especially if I decide the new fix or functionality needs to work differently than what was proposed.

Translations are welcomed with open arms. The more people that can access this powerful technology, the better!

If you don't have Github, or prefer not to use it, you can shoot me an email - cartertemm (at) gmail (dot) com.

Thanks for the support!