Final answer:
A storyboard for a research project on recognizing handwriting using deep learning would illustrate the journey from input to output, including steps such as image preprocessing, data augmentation, convolutional neural networks, recurrent neural networks or LSTM, classification, error correction, and final text output.
Step-by-step explanation:
Storyboarding for Handwriting Recognition Using Deep Learning
To create a storyboard for a research project titled "Recognizing Students/Doctor’s Handwriting Using Deep Learning," you would need to illustrate the process of how the system will function from input to output. Initially, the storyboard might start with a depiction of the input, which could be handwritten notes from students or doctors. The following frames would then detail the ensuing steps, such as:
- Image preprocessing, where the handwritten input is cleaned and normalized to ensure consistency before it is fed to the deep learning model.
- Data augmentation to enhance the variety of handwriting styles the system is exposed to during training.
- Application of convolutional neural networks (CNN) for feature extraction from the handwriting samples.
- Implementation of a recurrent neural network (RNN) or Long Short-Term Memory (LSTM) network to analyze sequences in the handwriting for character recognition.
- Integration of a classification layer to interpret the features and output the recognized text.
- Error correction and validation, where the system's outputs are compared against known text to improve accuracy.
- Final output, displaying the transcribed text on a screen for the user to review.
Throughout each frame of the storyboard, it is critical to provide explanatory notes and diagrams that clearly showcase the flow and mechanisms of the handwriting recognition system.