Attendee Control Final talk for the Bachelors Thesis by Leon Nissen - - PowerPoint PPT Presentation

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Chair of Network Architectures and Services Department of Informatics Technical University of Munich Attendee Control Final talk for the Bachelors Thesis by Leon Nissen Monday 9 th December, 2019 Chair of Network Architectures and Services

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Chair of Network Architectures and Services Department of Informatics Technical University of Munich

Attendee Control

Final talk for the Bachelor’s Thesis by

Leon Nissen

Monday 9th December, 2019 Chair of Network Architectures and Services Department of Informatics Technical University of Munich

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Problem definition

TUMexam is based on the principle that handwritten exams are digitized by scanning. To identify the exam and the corresponding student, TUMexam uses stickers with information to be attached to the first page of the exam. The student has to sign his attendance on the tutors sheet.

The paper on which the information is printed is expensive.
Printing all stickers takes a long time.
The sticking process is time-consuming.

Figure 1: TUMexam sticker on exam sheet

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SLIDE 3

Visionary scenario

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Related work

Tool Advantage Disadvantage ML model accurate needs data Tesseract integration accuracy CRAFT accurate slow Apple Vision FW

n-device

iOS only Cloud services accurate privacy

Table 1: Related work advantages & disadvantages (a) Apple Vision framework [1] (b) CRAFT – Character-Region Awareness For Text detection

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Workflow

Recognize ID card
Find registration number
Cut registration number
Recognize digit

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Workflow – Recognize ID card

Figure 2: Recognize ID card – Google Cloud Vision API [2]

Dcomplete Blue White Sum Correct 55 36 91 Wrong 1 1

Table 2: Student ID card type identification.

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Workflow – Find registration number

(a) morphologyEx function applied on student ID. (b) Highlight all text.

Dcomplete Blue White Sum Correct 47 36 83 Wrong 9 9

Table 3: Find registration number

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SLIDE 8

Workflow – Cut registration number

Figure 3: Naive Cutting (a) Frog Window Cutting (b) Sliding Window Cutting Figure 4: Other cutting approaches

Dnumber Blue White Sum Correct 376 288 664 Wrong

Table 4: Cut registration number into digits.

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Workflow – Recognize digit

Generated-Data (total: 620,000) Real-Data (total: 310,000) Ddigit Blue White Sum Correct 374 287 661 Wrong 2 1 3

Table 5: Machine learning model evaluation for single digits.

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Statistics – Overall result

Recognize ID card = 98.91%
Find registration number = 90.21%
Cut registration number = 100%
Recognize digit = 99.54%

For calculation we use the accuracy of all systems.

0.9891 · 0.9021 · 1 · 0.99548 = 0.8599 ≈ 86 %

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SLIDE 11

Limitations & problems

Figure 5: Misrecognized ID card. (a) Shows 1 (b) Shows 3 (c) Shows 5 Figure 6: Misrecognized digits

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Future work

Evaluate the current frameworks to find and cut out the student ID on an iPhone (Felix

Schrimper)

Develop a Minimum Viable Product (MVP)
Test the MVP in production
Based on an iOS app, create an Android application

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References

Apple. Documentation Vision Framework.

https://developer.apple.com/documentation/vision. Accessed: Oct. 30, 2019.

Google. Vision AI. https://cloud.google.com/vision/. Accessed: Dec. 9, 2019.

sckit learn. A demo of K-Means clustering on the handwritten digits data. https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_digits.html. Accessed: Dec. 9, 2019.

OpenCV. Morphological Transformations.

https://docs.opencv.org/trunk/d9/d61/tutorial_py_morphological_ops.html. Accessed: Nov. 10, 2019.

OpenCV. Image Processing - Sobel Derivatives. https://docs.opencv.org/2.4/doc/

tutorials/imgproc/imgtrans/sobel_derivatives/sobel_derivatives.html. Accessed:

Nov. 4, 2019.

Technical University of Munich. Schrift und Satz. https://portal.mytum.de/corporatedesign/index_html/vorlagen/index_schrift. Accessed: Dec. 9, 2019.

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Backup – Recognize ID card

clt = KMeans(n_clusters=1)

Figure 7: K-Means clustering [3]

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Backup – Find registration number – morphologyEx

blackhat = cv.morphologyEx(img, cv.MORPH_BLACKHAT, kernel) It is the difference between the closing of the input image and input image. [4]

Figure 8: MorphologyEx function [4]

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Backup – Find registration number – Sobel

sobel = cv2.Sobel(blackhat, ddepth=cv2.CV_32F, dx=1, dy=0, ksize=-1) The Sobel Operator is a discrete differentiation operator. It computes an approximation of the gradient of an image intensity function. [5]

Figure 9: Sobel function [5]

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Backup – Recognize digit – Font

The font of the student card comes closest to the font of the TUM’s own font. This can be found

n the TUM website under corporate design. [6]

Figure 10: TUM Neue Helvetica [6]

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