ML Model Accuracy/Results
Handwritten Alphanumeric model
Iteration 1:
Reasoning - As model is trained on the existing dataset, doesn't perform good on NIST dataset
Iteration 2:
Reasoning - After training the model on NIST misclassifications, improvement in accuracy
Iteration 3:
Reasoning - After training the model on NIST misclassifications as per previous checkpoint, improvement in accuracy
Iteration 4:
Reasoning - Training the model upon previous checkpoint and adding manually collected data, improvement in accuracy
Handwritten Digits model
Iteration 1:
Reasoning - As model is trained on the existing dataset, doesn't perform good on NIST dataset
Iteration 2:
Reasoning - After training the model on NIST misclassifications, improvement in accuracy
Iteration 3:
Reasoning: After training the model on NIST misclassifications and production dataset, improvement in accuracy
Iteration 4:
Reasoning: As averaging upon a large production dataset, the accuracy slightly dips as compared to iteration 3
Sample dataset images
Existing dataset
Handwritten alphanumeric
Handwritten digits
NIST dataset
Handwritten alphanumeric
Handwritten digits
Manually collected
Handwritten alphanumeric
Handwritten digits
Some unhandled misclassifications
Reasoning: Generally occurs if the digits are written in corners of the cell
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