Alex- Winter term 2025

Weekly Progress Summary — Internship Report 

Report

Period: 5^th – 9^th Jan 2026

Work Summary

1.     Participated in the CUSA selling exhibition event.

2.     Assisted with product packaging and preparation for sale.

3.     Supported product delivery and logistics for the event.

4.     Helped with booth setup and on-site arrangement during the exhibition.

Key Achievements

1.     Contributed to smooth operation and execution of the CUSA selling exhibition.

2.     Ensured products were properly prepared, delivered, and displayed for sale.

 

 

Period: 22^nd Dec 2025 – 2^nd Jan 2026

Work Summary

 

1.     Assisted in preparation for the CUSA event.

2.     Prepared product packaging and ensured items were properly packed.

3.     Checked products for accuracy and completeness prior to the event.

4.     Attached and verified food labels according to requirements.

5.     Conducted final verification of cleaned CSV files to ensure data accuracy and completeness.

Key Achievements

1.     Supported smooth preparation and setup for the CUSA event.

2.     Ensured products were correctly packaged and labelled for distribution.

3.     Completed final checks on cleaned CSV datasets before submission/use.

Tools & Methods

 

1.     Manual packaging and product handling

2.     Product checking and quality verification

3.     Food labelling and compliance checks

4.     CSV data review and validation

 

Period: 15^th – 19^th Dec 2025

Work Summary

1. Conducted systematic data cleaning using the developed OCR review tool.
2. Reviewed and corrected OCR-extracted food data to improve accuracy and consistency.
3. Drafted the outline for literature research related to the project.
4. Assisted with routine daily operational tasks as required.

 

Key Achievements

1. Made steady progress in cleaning and validating the OCR food dataset.
2. Established a clear structural outline for upcoming literature research.
3. Provided operational support to ensure smooth daily workflow.

 

Tools & Methods

1. Google Colab (Python notebook)
2. OCR data cleaning and review tool
3. Manual data validation and correction
4. Literature review planning and outlining

 

Issues & Observations

1. Data cleaning remains time-intensive due to OCR inconsistencies.
2. Further refinement of the cleaning process may improve efficiency.

 

Period: 9^th – 12^th Dec 2025

 

Work Summary

1.     Set up and validated Google Colab working environment and data access.

2.     Loaded and reviewed merged OCR food dataset structure.

3.     Tested and optimized image slicing parameters to improve tile alignment and readability.

4.     Validated slicing accuracy using preview and demo functions.

5.     Initiated structured OCR review workflow and began systematic data cleaning.

6.     Corrected OCR-extracted food names and values where necessary.

7.     Implemented regular data saving to ensure version control.

 

Key Achievements

1.     Established a stable and repeatable OCR review workflow.

2.     Improved image slicing accuracy, reducing misalignment-related OCR errors.

3.     Successfully initiated data cleaning on production dataset.

 

Tools & Methods

1.     Google Colab (Python notebook)

2.     OCR image slicing and review utilities

3.     Manual data validation and correction

4.     AI assistance for debugging and workflow optimization

 

Issues & Observations

1.     OCR accuracy varies with image quality and cropping consistency.

2.     Some entries require manual parameter adjustment for optimal slicing.