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Authorial approach to the detection of selected psychological traits based on handwritten texts

Woda Marek, Oliwa Grzegorz

International Journal of Electronics and Telecommunications

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2024

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Vol. 70, No. 1

145--152

EN

The study sought to use computer techniques to detect selected psychological traits based on the nature of the writing and to evaluate the effectiveness of the resulting software. Digital image processing and deep neural networks were used. The work is complex and multidimensional in nature, and the authors wanted to demonstrate the feasibility of such a topic using image processing techniques and neural networks and machine learning. The main studies that allowed the attribution of psychological traits were based on two models known from the literature, KAMR and DA. The evaluation algorithms that were implemented allowed the evaluation of the subjects and the assignment of psychological traits to them. The DA model turned out to be more effective than the KAMR model.

2

Text area detection in handwritten documents scanned for further processing

Pach Jakub Leszek, Krupa Artur, Antoniuk Izabella

Machine Graphics and Vision

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2020

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Vol. 29, No. 1/4

21--31

EN

In this paper we present an approach to text area detection using binary images, Constrained Run Length Algorithm and other noise reduction methods of removing the artefacts. Text processing includes various activities, most of which are related to preparing input data for further operations in the best possible way, that will not hinder the OCR algorithms. This is especially the case when handwritten manuscripts are considered, and even more so with very old documents. We present our methodology for text area detection problem, which is capable of removing most of irrelevant objects, including elements such as page edges, stains, folds etc. At the same time the presented method can handle multi-column texts or varying line thickness. The generated mask can accurately mark the actual text area, so that the output image can be easily used in further text processing steps.

3

Line segmentation of handwritten text using histograms and tensor voting

Babczyński Tomasz, Ptak Roman

International Journal of Applied Mathematics and Computer Science

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2020

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Vol. 30, no. 3

585--596

EN

There are a large number of historical documents in libraries and other archives throughout the world. Most of them are written by hand. In many cases they exist in only one specimen and are hard to reach. Digitization of such artifacts can make them available to the community. But even digitized, they remain unsearchable, and an important task is to draw the contents in the computer readable form. One of the first steps in this direction is to recognize where the lines of the text are. Computational intelligence algorithms can be used to solve this problem. In the present paper, two groups of algorithms, namely, projection-based and tensor voting-based, are compared. The performance is evaluated on a data set and with the procedure proposed by the organizers of the ICDAR 2009 competition.

4

Projection-based text line segmentation with a variable threshold

Ptak R., Żygadło B., Unold O.

International Journal of Applied Mathematics and Computer Science

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2017

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Vol. 27, no. 1

195--206

EN

Document image segmentation into text lines is one of the stages in unconstrained handwritten document recognition. This paper presents a new algorithm for text line separation in handwriting. The developed algorithm is based on a method using the projection profile. It employs thresholding, but the threshold value is variable. This permits determination of low or overlapping peaks of the graph. The proposed technique is shown to improve the recognition rate relative to traditional methods. The algorithm is robust in text line detection with respect to different text line lengths.