Project

# Title Team Members TA Documents Sponsor
6 Automated Intelligent Document Stamping System with Machine Vision Integration
Jiaheng Zeng
Peter Chen
Xuliang Huang
Zhiqiang Qiu
 appendix1.pdf
design_document1.docx
design_document2.pdf
final_paper1.pdf
other1.pdf
photo1.jpg
proposal1.pdf
video
 Fangwei Shao
This project aims to design and build a mechatronic system capable of automating the manual
process of stamping multi-page documents.

By integrating UI interface, machine vision, and an automatic page-turner, the system will
eliminate repetitive labor and ensure precise and convenient stamp placement.

The main work of mechanical task is to build X-Y positioning arms so the stamp head can move
on the XY plane and press at the target location. And paper feeding and output path are also
required. Similar to a printer, a pickup roller brings one sheet to the stage for stamp. After
stamping, an output roller pushes the sheet to the exit tray.

In the UI interface, the users can learn how to use this machine. And then, the users may choose
different document stamping modes in the UI interface (we may develop different modes based
on the demands).

Functional Requirements:
1. Motion Control:
• XY-axis movement to cover standard or non-standard page sizes (usually A4/Letter).
• Z-axis pressure detection for the adjustable stamping motion.
2. Vision & Logic:
• Mode A (Content-Aware): Detect text content and stamp in appropriate open spaces.
• Mode B (Keyword): Recognize specific characters (OCR) and stamp at a relative offset.
• Combine a VLM and a camera to achieve stamping location detection.
3. Pages Handling:
• Separate paper to ensure feeding only one paper into the stamping stage and withdrawing
paper from the stamping stage. The whole process should avoid jamming and be
transparent to the users for validation.
4. User Interaction:
• Software interface to select modes, input keywords, and monitor progress.

Goal:
Ultimately, we hope this project can help our ZJUI administrative staffs to liberate their hands
from repetitive stamping tasks, saving their time.

A Wearable Device Outputting Scene Text For Blind People

Hangtao Jin, Youchuan Liu, Xiaomeng Yang, Changyu Zhu

A Wearable Device Outputting Scene Text For Blind People

Featured Project

# Revised

We discussed it with our mentor Prof. Gaoang Wang, and got a solution to solve the problem

## TEAM MEMBERS (NETID)

Xiaomeng Yang (xy20), Youchuan Liu (yl38), Changyu Zhu (changyu4), Hangtao Jin (hangtao2)

## INSTRUCTOR

Prof. Gaoang Wang

## LINK

This idea was pitched on Web Board by Xiaomeng Yang.

https://courses.grainger.illinois.edu/ece445zjui/pace/view-topic.asp?id=64684

## PROBLEM DESCRIPTION

Nowadays, there are about 12 million visually disabled people in China. However, it is hard for us to see blind people in the street. One reason is that when the blind people are going to the location they are not familiar with, it is difficult for blind people to figure out where they are. When blind people travel, they are usually equipped with navigation equipment, but the accuracy of navigation equipment is not enough, and it is difficult for blind people to find the accurate position of the destination when they arrive near the destination. Therefore, we'd like to make a device that can figure out the scene text information around the destination for blind people to reach the direct place.

## SOLUTION OVERVIEW

We'd like to make a device with a micro camera and an earphone. By clicking a button, the camera will take a picture and send it to a remote server to process through a communication subsystem. After that, text messages will be extracted and recognized from the pictures using neural network, and be transferred to voice messages by Google text-to-speech API. The speech messages will then be sent back through the earphones to the users. The device can be attached to glasses that blind people wear.

The blind use the navigation equipment, which can tell them the location and direction of their destination, but the blind still need the detail direction of the destination. And our wearable device can help solve this problem. The camera is fixed to the head, just like our eyes. So when the blind person turns his head, the camera can capture the text of the scene in different directions. Our scenario is to identify the name of the store on the side of the street. These store signs are generally not tall, about two stories high. Blind people can look up and down to let the camera capture the whole store. Therefore, no matter where the store name is, it can be recognized.

For example, if a blind person aims to go to a book store, the navigation app will tell him that he arrives the store and it is on his right when he are near the destination. However, there are several stores on his right. Then the blind person can face to the right and take a photo of that direction, and figure out whether the store is there. If not, he can turn his head a little bit and take another photo of the new direction.

![figure1](https://courses.grainger.illinois.edu/ece445zjui/pace/getfile/18612)

![figure2](https://courses.grainger.illinois.edu/ece445zjui/pace/getfile/18614)

## SOLUTION COMPONENTS

### Interactive Subsystem

The interactive subsystem interacts with the blind and the environment.

- 3-D printed frame that can be attached to the glasses through a snap-fit structure, which could holds all the accessories in place

- Micro camera that can take pictures

- Earphone that can output the speech

### Communication Subsystem

The communication subsystem is used to connect the interactive subsystem with the software processing subsystem.

- Raspberry Pi(RPI) can get the images taken by the camera and send them to the remote server through WiFi module. After processing in the remote server, RPI can receive the speech information(.mp3 file).

### Software Processing Subsystem

The software processing subsystem processes the images and output speech, which including two subparts, text recognition part and text-to-speech part.

- A OCR recognition neural network which is able to extract and recognize the Chinese text from the environmental images transported by the communication system.

- Google text-to-speech API is used to transfer the text we get to speech.

## CRITERION FOR SUCCESS

- Use neural network to recognize the Chinese scene text successfully.

- Use Google text-to-speech API to transfer the recognized text to speech.

- The device can transport the environment pictures or video to server and receive the speech information correctly.

- Blind people could use the speech information locate their position.