The problem QualiText solves

In the metaverse, digitization of text documents is vital to extract information accurately. Poor quality documents, however, often result in incorrect results with Optical Character Recognition (OCR) and other data extraction methods. To tackle this challenge, an automated quality assessment process is essential to analyze and assess document quality before utilizing OCR or other techniques. By implementing this process, it ensures the critical information extracted is accurate and eliminates the risk of subpar document quality. Utilizing AI and machine learning algorithms, the assessment process will continuously improve and provide reliable and efficient results for the user.

Challenges we ran into

The creation of a highly accurate product presented two key challenges for our team. Firstly, obtaining a diverse and representative data set for training our models was a significant obstacle. The data set had to cover a wide range of image qualities, from excellent to poor, in order to effectively train our models to recognize and assess document quality. The scarcity of such data sets on the market led us to create our own, which required a significant investment of time and resources.

The second challenge was achieving a high level of accuracy in our product. This involved extensive research, development, and testing in order to fine-tune our algorithms and models. Our team worked diligently to explore various approaches and technologies, ultimately settling on a solution that leveraged the latest advancements in AI and machine learning. Despite the challenges and obstacles, our team never lost sight of their goal, and their dedication and expertise resulted in a product that exceeded our expectations, with an accuracy level of up to 93%.