In this thesis different architectures of Convolutional Neural Networks (CNN) and their suitability for object recognition were investigated by using the example of Egyptian hieroglyphs.

First, basic principles for artificial neural networks and the components of CNNs, such as convolutional layers, are introduced and explained, followed by explanations of the used data sets and the associated difficulties. We then present libraries for the concrete implementation and use of artificial neural networks and describe the sequence of the evaluation of object recognition.

The CNNs were trained and evaluated with different numbers of classes and the associated number of images. The experiments are divided by the three used training methods. For the first, the CNNs were trained with the help of autoencoders. For the second, the CNNs were trained block-wise, and for the third deeper network architectures were investigated. Various network architectures such as Residual Networks (ResNet) and Densely Connected Convolutional Networks, described in the literature, were implemented and evaluated.

The results of the experiments show that it is possible to train CNNs with up to 69 convolutional layers, to classify about 6500 different Egyptian hieroglyphs and finally to carry out an object recognition with very good results. The best results for object detection 0.92362 were achieved for a CNN with 6465 classes and 13 convolutional layers.

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In this thesis different machine learning algorithms are evaluated for the task of multi-label classification. The evaluation is done with the binary classifiers naive Bayes and support vector machine (SVM) and the multi-class classifier supervised latent Dirichlet allocation (SLDA). To enable naive Bayes and SVM to do multi-label classification the RAkEL transformation is used and for SLDA a topic model multi-label learner is developed and used.

The Reuters-21578 corpus is used. Since not all texts have labels and not all labels occur in sufficient frequency a selection of texts was used. Two corpora were created and used for classification.

The classification results show that the best results are archived with SVM. Naive Bayes and SLDA give very similar results, but SLDA has a very long runtime.

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