Expensya is a Cloud based multi-platform expense management software. The web and mobile solution covers the entire travel and expense journey: before, during and after each business trip.

One of the main features of Expensya is the automatic extraction of information out of the receipts. Currently, this is done with OCR+. With the emergence of relevant deep learning techniques, Expensya would like to explore the performance of Deep Learning to improve one of the main features of the application, which is the automatic classification of the receipts into different categories, right after snapping a picture of the receipt.

The objective of this challenge is to create a machine learning model to classify images of receipts by the category of the expense.

About Expensya (https://www.expensya.com/en)

Expensya is a French-Tunisian startup that automates expense reports, for 5000 companies in 100 countries, using AI. The solution enables professionals to get rid of this time-consuming task and to manage expense claims effectively. Using powerful AI and Machine Learning algorithms, the Expensya app integrates digitization, archiving, and smart processing technologies, which automatically capture expenses, among other data, in 1 second. Expensya also facilitates the data transmission and integrates perfectly with the accounting software.

The evaluation metric for this challenge is Log Loss.

Your submission file should look like:

img_id   cat_1  cat_2  cat_3  cat_4
img_1    1      0      0      0
img_2    0      0      1      0

For example, the above means that:

• img_1 belongs to cat_1
• img_2 belongs to cat_3

This is a learning competition. Aside from knowledge, there are no prizes for this competition.

You will receive 25 points for your first submission and 50 points for your first non-sample submission. You can read more about Zindi points here.

As this is a knowledge competition it will not close.

We reserve the right to update the contest timeline if necessary.

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As this is a learning challenge, aside from the rules in the Terms of Use, no other particular rules apply.

This challenge is open to all and not restricted to any country.

Teams and collaboration

You may participate in this competition as an individual or in a team of up to four people. When creating a team, the team must have a total submission count less than or equal to the maximum allowable submissions as of the formation date. A team will be allowed the maximum number of submissions for the competition, minus the highest number of submissions among team members at team formation.

Multiple accounts per user are not permitted, and neither is collaboration or membership across multiple teams. Individuals and their submissions originating from multiple accounts will be disqualified.

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Datasets and packages

The solution must use publicly-available, open-source packages only. Your models should not use any of the metadata provided.

You may use only the datasets provided for this competition. Automated machine learning tools such as automl are not permitted.

If the challenge is a computer vision challenge, image metadata (Image size, aspect ratio, pixel count, etc) may not be used in your submission.

You may use pretrained models as long as they are openly available to everyone.

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You must notify Zindi immediately upon learning of any unauthorised transmission of or unauthorised access to the competition data, and work with Zindi to rectify any unauthorised transmission or access.

Your solution must not infringe the rights of any third party.

Submissions and winning

You may make a maximum of 10 submissions per day.

Note that to count, your submission must first pass processing. If your submission fails during the processing step, it will not be counted and not receive a score; nor will it count against your daily submission limit. If you encounter problems with your submission file, your best course of action is to ask for advice on the Competition’s discussion forum.

Note that there is no public/private leaderboard split for this challenge. Read more about public and private leaderboards in this post.

You acknowledge and agree that Zindi may, without any obligation to do so, remove or disqualify an individual, team, or account if Zindi believes that such individual, team, or account is in violation of these rules. Entry into this competition constitutes your acceptance of these official competition rules.

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Please refer to the FAQs and Terms of Use for additional rules that may apply to this competition. We reserve the right to update these rules at any time.