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PoC Elaboration: Passport and Visa Analyser

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Challenge Overview

In the previous ideation challenge we have developed a proof-of-concept Python / OpenCV / PyTorch based solution (provided in the challenge forum) for the automated recognition and machine-reading of scanned passport and visas.

Our client praised the outcome of that challenge and decided to run this follow-up competition to further elaborate the idea and prototype app. The outlined focus points are:
  1. Improvements of data recognition and capture:
    1. Current solution does not work for some images now: for example, it fails for tilted passport pages. It should be improved to be more robust for image alignment and quality;
    2. It reads only data from Machine-Readable Zones (MRZ). For example, issue date and expiration dates are not encoded into MRZ, but they are generally present in all passports and visas; in general the client wants to be able to read all pages of passport, even those not containing MRZ;
    3. Data capturing should be zonal-based rather than template-based (i.e. we should avoid the need to manually provide template for each possible kind of passport / visa page, instead we would like to have a solution that is able to automatically detect and parse different fields it finds at a page);
    4. Some passports / visas may have barcodes with encoded infromation (e.g. Brazilian passports), we should support reading ot them, when available.
    5. Be sure that the format of extracted passport / visa data is uniform between different passports and visas across all countries.
  2. Technical improvements:
    1. Current prototype app supports only JPEG images. The client wants to add support for PNG and PDF formats;
    2. Dates should be converted to the standard format (dd/mon/yyyy) across all passports and visas;
    3. Solution should be deployable on Microsoft Azure.
  3. The client wants to further look for a hardware solution / system to scan all pages of passport without a manual intervention. Keep in mind that we are interested in a specific, reasonably priced device (i.e. a big and expensive machine intended for automated digitalization of big books in library, most probably does not match the expectations).
The challenge will be judged subjectively by community reviewers. Reviewers will have to evaluate, whether your modifications of the prototype, and suggestions on the point #3 provide an adequate additional value to the outcomes of the previous challenge (we perfectly understand that it might be not feasible to resolve all points raised by the customer, at least within a single challenge; it is fine as long as your solution demonstrates solid improvements, that, among other things, proove the perspective of further work on the idea).

Don’t hesitate to clarify any doubts via the challenge forum, or via Contact Managers option in Online Review system, if you have sensitive questions, that should not be shared with other competitors.

Final Submission Guidelines

Submit updated version of the prototype app, along with document(s) (and probably a video) explaining the improvements done; how they can be verified; and what are you ideas about the point #3.

Reliability Rating and Bonus

For challenges that have a reliability bonus, the bonus depends on the reliability rating at the moment of registration for that project. A participant with no previous projects is considered to have no reliability rating, and therefore gets no bonus. Reliability bonus does not apply to Digital Run winnings. Since reliability rating is based on the past 15 projects, it can only have 15 discrete values.
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ELIGIBLE EVENTS:

2018 Topcoder(R) Open

REVIEW STYLE:

Final Review:

Community Review Board
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Approval:

User Sign-Off
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CHALLENGE LINKS:

Review Scorecard

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