SAP Support Services-ID Card Detection for CumulusPro
SAP Support Services-ID Card Detection for CumulusPro
Find out the features & challenges of implementing Image Processing Border Detection- custom web app development for Cumuluspro.
Implementation
About Client
- https://cumuluspro.com/solutions/identify-plus/
- Location:Singapore
Project Background- SAP Support Services-ID Card Detection
This project explains how we implemented border detection and able to process the image. This involved taking pictures from the browser camera, Ability to capture images using different browsers and devices and store the images in blob storage and relevant information on the SQL server. Processing of images digitally is of much importance since processes are digitalised and people take picture of different ID cards(driving license, passport etc) and upload for various purpose today(for instance filling online applications and forms for numerous reasons).First level of image processing algorithm is implemented on these images for seamless application processing before they are uploaded in the system due to the presence of various issues in the images like existence of white spaces, skewed image to name a few.
Scope & Requirement
- The user captures the image from the camera and should be able to upload the image, which should be
- stored in a suitable format. If the image is not correct, then it should either be asked to be retaken or
- be corrected using a series of algorithms that we implemented
- Ensure the image classification is done, as there were 2 Ids being scanned.
- Change the orientation of the images to de-skew the image to make it 0 degree
- Be able to refine the resolution of the images and detect borders to crop the image
- If not able to correct the image ask the user to retake the image before uploading it to the system
- If taken successfully, store the image in the BLOB server and do OCR to store the important information related to Identification in the corresponding server
- Pre-processing to convert the image to greyscale and reduce the noise and improve image quality and size using PyrDown and PyrUp
Implementation
Technology and Architecture
Technology/ Services used
- Mobile app was deployed with the below technological component
- Backend Code: .NET Core, C#, Node.js
- Mobile App code: React Native
- Web App code: ReactJS
- Database: SQL Server, MongoDB
- Cloud: Microsoft Azure
Integrations
- Integration to read all data from an existing Shopify backend.
- Single Sign-on using Auth0 to register using Google and follow same login procedure as on the Shopify web app.
- Sendgrid to send emails for order processing events
Security:
- All API endpoints are tokenized
- Payment Gateway like paypal would be used which are secure and PCI DSS compliant.
Backup and Recovery
Cloud systems and components used in the attendance management system are secure and 99.99% SLA. We have added HA/DR mechanism to create a replica of the services
Scalability
Cost Optimization
Code Management, Deployment
Code for the app is handed over to the client through Microsoft AppCenter.
CI/CD is implemented to automatically add, build and deploy any code changes on the app side.
Features of Image Processing Border Detection-custom app development
- Automatic ID card border detection and the image cropping
- Removal of skewness (if present)
- Correction of orientation
Challenges
We encountered some ssues as below:
- Establishing a pre-defined system criterion which needs to be met before uploading the picture. This includes deciding threshold value for the image processing algorithms to be implemented for removal of white spaces present on the side of ID card and skewness present in the image captured
- The Canny algorithm used for border detection had some limitations and didn’t work in all the scenarios where ID card had more prominent rectangle drawn on it or an image with white background of a white ID card. These cases didn’t gave a correct collection of lines and may have resulted in incorrect border detection. To overcome this problem we implemented FindContours on binary image which gave us a more reliable result
- We had to cater for devices of varying power and capability since this solution implementation had to run on various platforms. As we got into the testing phase we had to avoid large variation in responsiveness depending on the device used
Project Completion
Deliverables
We used OpenCV library for image processing along with EmguCV wrapper, which is compatible with .NET languages (C#,VB,VC++ etc). We implemented the solution through the following steps:
Down sample -> Noise reduction -> Up-sample -> Image Enlargement
- Edge detection using Canny algorithm(to convert image into collection of lines)
- Detect outermost lines for Border detection through ‘Probabilistic Hough transform’ to filter out lines based on width & length
- Using ‘FindContours’ to find objects at outer most location (to overcome the shortcomings of Hough transform)
- Post processing which includes cropping the image along the border and getting a processed image containing ID card completely and removing any skewness present
Support
- As part of the project implementation we have standard practice of providing 1 month of extended support. This includes any Major / Minor bug fixes.
Testimonial
We took Feedback from stakeholders as below:
Syed Mohd. Atif
Co-Founder Enticed Retail LLP
Next Phase
We are now looking at the next phase of the project, which involves:
- Images may not be of the same template, so machine learning can be added to identify the image template and process the image accordingly.
- For a specific template, there should be a training model which helps the system to know how the new image template would look like and to be able to adapt to new template images faster.
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Testimonial
Peritos is a team of highly skilled developers, technical experts, and delivery managers. We’ve been very impressed with their commitment. Their developers and delivery managers have conducted themselves with professionalism and diligence at all times, and the quality of the work they have performed has been excellent. Many times, they proposed better solutions, which resulted in better and faster products. Peritos is a reliable AWS Partner. You can trust and be satisfied.
“We are very happy with the results of this major IT project with Peritos Solutions. Their staff gained a good knowledge of our business and processes. They were able to work with many of our diverse team members and launch Business Central ERP on time and on budget according to our requirements. We will be using them again for future projects and additional development actions. Thank you!”
The project was completed on time and as per the agreed budget. The communication was excellent, as was the dedication to quickly turning around the required report development. The overall experience was good, and I would definitely work with the Peritos Team again.
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