Ane-Lexia

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  input_img

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  output_audio

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  output_pdf

Inspiration :

The inspiration for this project came from looking at the difficulty people with dyslexia face. I have a friend in my English class at the university whom I met recently and he actually has Dyslexia, I was curious about how a person with dyslexia reads or understands written language. After having a long conversation with him, I found out the accessibility features a person with dyslexia has access to, and whether it is effective. This gave me the inspiration to come up with a project to help people with dyslexia.

What it does:

Ane-Lexia is a web application that assists students with Dyslexia. This is designed to convert handwritten images to readable text using a dyslexic-friendly font. This specialized font enhances readability for individuals with dyslexia. Additionally, the application incorporates a speech feature, allowing the converted text to be read aloud for hearing assistance. Through a combination of optical character recognition (OCR) technology with dyslexic-friendly typography and speech synthesis, the application provides valuable support to dyslexic students, ensuring accessibility and improving their learning experience. It scans the image provided, for example, notes on the board from a professor, and outputs it in a dyslexia-friendly text.

How we built it:

We used Azure Vision API as our main OCR to convert image to text, we then used the text from the OCR as input and exported a pdf file with the text having OpenDyslexia font. We also created a text-to-speech converter to make it more accessible to people. We finally integrated the backend of Python using flask with HTML and CSS files, to create a web application.

Challenges we ran into

One of the major challenges we ran into was finding an OCR that could detect handwriting. Our original plan was to use pytesseract, but it gave very bad results. We had to go through a ton of APIs to get one suitable to our needs. Another problem we ran into was formatting the text and downloading a .ttf file for OpenDyslexia, to export in the PDF. However, the biggest challenge was to integrate the backend and the front end.

Accomplishments that we're proud of

We're amazingly proud of how minimalistic, straight to the point, and effective our app is. Based on some of our preliminary tests, we were getting almost 100% accuracy. What I'm most proud of is that we were able to create something that I am sure will be very helpful to a large population, and make their lives a little better.

What we learned

The lessons I learned from this hackathon were very valuable. Since it was my first hackathon ever in my life I did not have any hope to even make this submission, all I wanted to do was have a great time and get better. But as me and my team started ideating and working on a project, our determination started becoming stronger and we were steadfast in making sure to submit something, and I am proud that we have come this far. So, the main lesson is, that if we have determination a seemingly impossible task can become possible to do.

What's next for Ane-Lexia

Ane-Lexia will have a lot of things for it. We plan to have our own ML model to detect handwritten text, we also want to create iOS and Android apps to increase accessibility. We want to make our app easy to use and widely available to people who need it. I believe this will largely benefit the students in the education system who have dyslexia.

Built With

• api
• css3
• flask
• html5
• javascript
• python