## Inspiration

We want to make everyone impressed by our amazing project! We wanted to create a revolutionary tool for image identification!

## What it does

It will identify any pictures that are uploaded and describe them.

## How we built it

We built this project with tons of sweats and tears. We used Google Vision API, Bootstrap, CSS, JavaScript and HTML.

## Challenges we ran into

We couldn't find a way to use the key of the API. We couldn't link our html files with the stylesheet and the JavaScript file. We didn't know how to add drag and drop functionality. We couldn't figure out how to use the API in our backend. Editing the video with a new video editing app. We had to watch a lot of tutorials.

## Accomplishments that we're proud of

The whole program works (backend and frontend). We're glad that we'll be able to make a change to the world!

## What we learned

We learned that Bootstrap 5 doesn't use jQuery anymore (the hard way). :'(

## What's next for Scanspect

The drag and drop function for uploading iamges!
## Inspiration

The amount of data in the world today is mind-boggling. We are generating 2.5 quintillion bytes of data every day at our current pace, but the pace is only accelerating with the growth of IoT.

We felt that the world was missing a smart find-feature for videos. To unlock heaps of important data from videos, we decided on implementing an innovative and accessible solution to give everyone the ability to access important and relevant data from videos.

## What it does

CTRL-F is a web application implementing computer vision and natural-language-processing to determine the most relevant parts of a video based on keyword search and automatically produce accurate transcripts with punctuation.

## How we built it

We leveraged the MEVN stack (MongoDB, Express.js, Vue.js, and Node.js) as our development framework, integrated multiple machine learning/artificial intelligence techniques provided by industry leaders shaped by our own neural networks and algorithms to provide the most efficient and accurate solutions.

We perform key-word matching and search result ranking with results from both speech-to-text and computer vision analysis. To produce accurate and realistic transcripts, we used natural-language-processing to produce phrases with accurate punctuation.

We used Vue to create our front-end and MongoDB to host our database. We implemented both IBM Watson's speech-to-text API and Google's Computer Vision API along with our own algorithms to perform solid key-word matching.

## Challenges we ran into

Trying to implement both Watson's API and Google's Computer Vision API proved to have many challenges. We originally wanted to host our project on Google Cloud's platform, but with many barriers that we ran into, we decided to create a RESTful API instead.

Due to the number of new technologies that we were figuring out, it caused us to face sleep deprivation. However, staying up for way longer than you're supposed to be is the best way to increase your rate of errors and bugs.

## Accomplishments that we're proud of

* Implementation of natural-language-processing to automatically determine punctuation between words.
* Utilizing both computer vision and speech-to-text technologies along with our own rank-matching system to determine the most relevant parts of the video.

## What we learned

* Learning a new development framework a few hours before a submission deadline is not the best decision to make.
* Having a set scope and specification early-on in the project was beneficial to our team.

## What's next for CTRL-F

* Expansion of the product into many other uses (professional education, automate information extraction, cooking videos, and implementations are endless)
* The launch of a new mobile application
* Implementation of a Machine Learning model to let CTRL-F learn from its correct/incorrect predictions
## Inspiration

## What it does

Generates relevant, realtime hashtags from just an image using Google Cloud's Vision API's image recognition technology for use on any social media platform.

## How we built it

The general process is 1) send the selected image to multiple AI image-recognition service providers (Google Vision and Imagga), 2) the backend server receives the data from the providers, aggregates the results based on our algorithm that filters the "words" by context, 3) finally, all the selected "words" are transferred over to the front-end and is displayed accordingly on the app.

## Challenges we ran into

Angular deployment to server was problematic due to some compression issue. Also, most of the social APIs that we were going to use (i.e. Facebook, Twitter, Instagram) were either limited, or were tied to deprecated services that hindered us from getting richer and more accurate data for the users.

## Accomplishments that we're proud of

We were able to come up with the idea and FOCUS on what mattered (core function of the app) and what did not (using other APIs). This saved us time and allowed us to streamline our process of creating the app all the way from the front to the back. Also, linking up all the APIs that were a first-time to us really was an accomplishment for us.

## What we learned

We learned that the Google Vision API is really powerful. Having said that it wasn't enough to provide more rich data that had context, thus, looking into Imagga as well. We also learned that however many times we plan things, time is always against us, and we need to efficiently manage the time to produce a viable working product.

## What's next for InsTags