Handwritten Digit Recognizer
Hazzle-free understanding!
Created on 12th May 2023
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Handwritten Digit Recognizer
Hazzle-free understanding!
The problem Handwritten Digit Recognizer solves
Handwritten digit recognition involves training a machine learning model to recognize handwritten digits and convert them into digital format. This technology has numerous applications, including in postal services for reading addresses, in bank checks for digitizing the amount and account number, and in online forms for data entry. Additionally, it can be used in computer vision tasks such as identifying digits in images and recognizing hand-written mathematical equations. Overall, the handwritten digit recognizer can help automate tasks that involve manual digit recognition, increasing efficiency and reducing errors.
Challenges we ran into
These recognizer model faced several challenges, including overfitting, underfitting, and data bias. Overfitting occurs when a model is too complex and fits the training data too closely, resulting in poor performance on new, unseen data. Underfitting happens when a model is too simple and fails to capture the complexity of the data, leading to poor performance. Data bias is also a significant challenge as models trained on biased data can lead to biased predictions.
Technologies used