Project

# Title Team Members TA Documents Sponsor
35 Handwriting Robot With User-Customized Font Style
 Mingchen Sun
Xuancheng Liu
Zhixiang Liang
Zifan Ying
 design_document1.pdf
proposal1.pdf
 Gaoang Wang
# Handwriting Robot With User-Customized Font Style

## Team Members

- Zifan Ying (zifany4)
- Zhixiang Liang (zliang18)
- Xuancheng Liu (xl124)
- Mingchen Sun (msun52)

## Problem

Handwriting remains a personal and unique form of expression, yet current digital and automated writing solutions lack the ability to accurately replicate individual handwriting styles. Existing methods either rely on digital fonts that imitate handwriting or require complex, manual customizations. This project addresses the need for an automated system that can learn and reproduce a person's unique handwriting style with high fidelity. By integrating machine learning-based handwriting analysis with robotic writing mechanisms, this system enhances document personalization, enabling applications in personalized correspondence, secure document signing, and artistic reproduction.

## Proposed Solution

The proposed solution is a handwriting replication system that learns a user's unique writing style and reproduces it on new documents. The system analyzes sample handwriting using computer vision and machine learning, then employs a robotic mechanism to accurately recreate the learned style onto paper with a pen. The result is realistic, customizable handwritten output that closely mimics the original writing style.

## Solution Components

### Character Learning & Generation

- Utilizes computer vision and machine learning to extract and analyze the user’s handwriting style from provided sample documents.
- Generates new text in the learned style based on any user-provided input.
- Can run on a standard computer or an embedded microcontroller (MCU) for flexibility.

### Writing Mechanism

- A two-axis robotic system that writes text with precise, controlled pen movements.
- Optional third-axis control to adjust pen pressure or stroke width for enhanced authenticity of writing.
- Integrated paper-feeding mechanism to ensure smooth, continuous document production.

## Criteria of Success

- The system successfully learns and replicates handwriting styles from the user’s sample documents.
- The robotic writing mechanism accurately reproduces the generated text in a natural, human-like manner.
- The final output closely matches the user’s real handwriting in style, spacing, and (if applicable) pen pressure.

Electronic Automatic Transmission for Bicycle

Featured Project

Tianqi Liu(tliu51)

Ruijie Qi(rqi2)

Xingkai Zhou(xzhou40)

Sometimes bikers might not which gear is the optimal one to select. Bicycle changes gears by pulling or releasing a steel cable mechanically. We could potentially automate gear changing by hooking up a servo motor to the gear cable. We could calculate the optimal gear under current condition by using several sensors: two hall effect sensors, one sensing cadence from the paddle and the other one sensing the overall speed from the wheel, we could also use pressure sensors on the paddle to determine how hard the biker is paddling. With these sensors, it would be sufficient enough for use detect different terrains since the biker tend to go slower and pedal slower for uphill or go faster and pedal faster for downhill. With all these information from the sensors, we could definitely find out the optimal gear electronically. We plan to take care of the shifting of rear derailleur, if we have more time we may consider modifying the front as well.

Besides shifting automatically, we plan to add a manual mode to our project as well. With manual mode activated, the rider could override the automatic system and select the gear on its own.

We found out another group did electronic bicycle shifting in Spring 2016, but they didn't have a automatic function and didn't have the sensor set-up like ours. Commercially, both SRAM and SHIMANO have electronic shifting products, but these products integrate the servo motor inside the derailleurs, and they have a price tag over $1000. Only professionals or rich enthusiasts can have a hand on them. As our system could potentially serve as an add-on device to all bicycles with gears, it would be much cheaper.