newtrain Computer Vision Project

Hiếu

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ADDRESS
SELLER
TIMESTAMP
TOTAL_COST
Description

Here are a few use cases for this project:

  1. Expense Tracking and Reimbursement: Users can employ the "newtrain" model to automatically extract relevant data from receipts, such as total cost, date, and seller information. This data can be directly input into expense management tools or reimbursement software, streamlining the process for businesses and organizations.

  2. Post-Purchase Warranty Management: The "newtrain" model can be used to identify and store essential information from receipts, such as purchase date and location. This would make it easy for consumers to track warranty periods and initiate support or refund processes with the appropriate seller.

  3. Financial and Tax Management: The "newtrain" model can be utilized in tax preparation software or financial management apps to identify and categorize transactions from scanned receipts. This will help users easily manage and report their expenses, ensuring they meet tax obligations and maintain accurate financial records.

  4. Smart Home Assistants: Home automation systems and virtual assistants could utilize the "newtrain" model to interpret and store receipt data whenever a user scans or takes a photo of a receipt. Later, the user can ask their smart home assistant for details about specific purchases, helping them keep track of their expenses.

  5. Mail Sorting and Delivery: Postal services can benefit from using "newtrain" to recognize and classify shipment labels, addresses, and sender details. By swiftly identifying and sorting through mail content, automation can be introduced, enhancing the accuracy and efficiency of mail delivery processes.

Supervision

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Cite This Project

LICENSE
CC BY 4.0

If you use this dataset in a research paper, please cite it using the following BibTeX:

                        @misc{
                            newtrain-kzp6o_dataset,
                            title = { newtrain Dataset },
                            type = { Open Source Dataset },
                            author = { Hiếu },
                            howpublished = { \url{ https://universe.roboflow.com/hieu-yndtn/newtrain-kzp6o } },
                            url = { https://universe.roboflow.com/hieu-yndtn/newtrain-kzp6o },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { jan },
                            note = { visited on 2024-11-28 },
                            }
                        
                    

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