Navigation

Digitization of Handwritten Rey Osterrieth Complex Figure Test Score Sheets

Type: BA thesis

Status: running

Date: September 15, 2021 - February 15, 2022

Supervisors: Vincent Christlein, Daniel Stromer, Andreas Maier

The Rey Osterrieth Complex Figure Test (ROCF) is a neuropsychological test to detect cognitive
impairments.
As the scoring is mostly implemented by hand from experts the goal is to automate the ROCF by
means of machine learning.
The whole project consists of four milestones:
1. State-of-the-art literature research
2. Development of an OCR-based algorithm to digitize the handwritten score sheet into machine
readable structured format for training an automatic algorithm
3. Development of a deep learning algorithm for automatic scoring ROFCs based on the 36-point
scoring system
4. Evaluation of the algorithm based on the data and publication of the results
This thesis will mainly examine the first two steps.
The used scoring sheets consist of an identical structure and just the score itself is handwritten.
Therefore only digits have to be recognized.
The idea is to use networks already trained on the MNIST database (e.g. [1], [2], [3]) and to gain the
best outcome performance for the described issue.
Therefore some preprocessing of the scanned scoring sheets such as detecting areas of interest, binari-
zation or rotation will be necessary to match the requirements for input data of the specific algorithms
as well as for improving performance.
Other options for preprocessing could be template matching or taking advantage of the HU-moments
[4]. Hereby text detection, i.e. finding areas of interests, is one of the typically performed steps in any
text processing pipeline [5].
Furthermore modifying algorithms and weights will be used to achieve different outcomes which than
can be compared in relation to their performances.
The implementation should be done in Python.

References
[1] Gargi Jha. Mnist handwritten digit recognition using neural network, Sep 2020.
[2] Muhammad Ardi. Simple neural network on mnist handwritten digit dataset, Sep 2020.
[3] Dan Claudiu Ciresan, Ueli Meier, Luca Maria Gambardella, and Jürgen Schmidhuber. Deep big simple
neural nets excel on handwritten digit recognition. CoRR, abs/1003.0358, 2010.
[4] Zengshi Chen, Emmanuel Lopez-Neri, Snezana Zekovich, and Milan Tuba. Hu moments based handwritten
digits recognition algorithm. In Recent advances in knowledge engineering and systems science: Proceedings
of the 12TH international conference on artificial intelligence, knowledge engineering and data bases, page
98–104. WSEAS Press, 2013.
[5] Simon Hofmann, Martin Gropp, David Bernecker, Christopher Pollin, Andreas Maier, and Vincent Christlein.
Vesselness for text detection in historical document images. In 2016 IEEE International Conference on
Image Processing (ICIP), pages 3259–3263, 2016.