Text recognition in real-life images poses a challenging task due to blur, distortion, and low resolution. This work presents an innovative method integrating image super-resolution, image restoration, and optical character recognition techniques to enhance text recognition in real-life photographs. We specifically reviewed the processing of the TextZoom dataset and utilized transfer learning on an improved version of the image super-resolution model, SwinIR. The findings of our experiment show that our text recognition scores are better than the current best scores, and there is a significant rise in the peak signal-to-noise ratio while dealing with deformed low-resolution images from the TextZoom dataset. This approach outperforms earlier research in the domain of scene text image super-resolution and offers a promising resolution for text recognition in real-life images. The code can be accessed at this location: https://github.com/Phimanu/TextSR

Recognizing disturbed text in real-life images is a difficult problem, as information that is missing due to low resolution or out-of-focus text has to be recreated. Combining text super-resolution and optical character recognition deep learning models can be a valuable tool to enlarge and enhance text images for better readability, as well as recognize text automatically afterwards. We achieve improved peak signal-to-noise ratio and text recognition accuracy scores over a state-of-the-art text super-resolution model TBSRN on the real-world low-resolution dataset TextZoom while having a smaller theoretical model size due to the usage of quantization techniques. In addition, we show how different training strategies influence the performance of the resulting model.

Human lives are increasingly influenced by algorithms, which therefore need to meet higher standards not only in accuracy but also with respect to explainability. This is especially true for high-stakes areas such as real estate valuation. Unfortunately, the methods applied there often exhibit a trade-off between accuracy and explainability.

Several large-scale machine learning tasks, such as data summarization, can be approached by maximizing functions that satisfy submodularity. These optimization problems often involve complex side constraints, imposed by the underlying application. In this paper, we develop an algorithm with poly-logarithmic adaptivity for non-monotone submodular maximization under general side constraints. The adaptive complexity of a problem is the minimal number of sequential rounds required to achieve the objective. Our algorithm is suitable to maximize a non-monotone submodular function under a p-system side constraint, and it achieves a (p + O(sqrt(p)))-approximation for this problem, after only poly-logarithmic adaptive rounds and polynomial queries to the valuation oracle function. Furthermore, our algorithm achieves a (p + O(1))-approximation when the given side constraint is a p-extendable system. This algorithm yields an exponential speed-up, with respect to the adaptivity, over any other known constant-factor approximation algorithm for this problem. It also competes with previous known results in terms of the query complexity. We perform various experiments on various real-world applications. We find that, in comparison with commonly used heuristics, our algorithm performs better on these instances.

We investigate language learning in the limit from text with various cautious learning restrictions. Learning is cautious if no hypothesis is a proper subset of a previous guess. While dealing with a seemingly natural learning behaviour, cautious learning does severely restrict explanatory (syntactic) learning power. To further understand why exactly this loss of learning power arises, Kötzing and Palenta (2016) introduced weakened versions of cautious learning and gave first partial results on their relation. In this paper, we aim to understand the restriction of cautious learning more fully. To this end we compare the known variants in a number of different settings, namely full-information and (partially) set-driven learning, paired either with the syntactic convergence restriction (explanatory learning) or the semantic convergence restriction (behaviourally correct learning). To do so, we make use of normal forms presented in Kötzing et al. (2017), most notably strongly locking and consistent learning. While strongly locking learners have been exploited when dealing with a variety of syntactic learning restrictions, we show how they can be beneficial in the semantic case as well. Furthermore, we expand the normal forms to a broader range of learning restrictions, including an answer to the open question of whether cautious learners can be assumed to be consistent, as stated in Kötzing et al. (2017).

We study the problem of maximizing a non-monotone submodular function under multiple knapsack constraints.We propose a simple discrete greedy algorithm to approach this problem, and prove that it yields strong approximation guarantees for functions with bounded curvature.In contrast to other heuristics, this does not require problem relaxation to continuous domains and it maintains a constant-factor approximation guarantee in the problem size.In the case of a single knapsack, our analysis suggests that the standard greedy can be used in non-monotone settings.Additionally, we study this problem in a dynamic setting, in which knapsacks change during the optimization process.We modify our greedy algorithm to avoid a complete restart at each constraint update.This modification retains the approximation guarantees of the static case.We evaluate our results experimentally on a video summarization and sensor placement task.We show that our proposed algorithm competes with the state-of-the-art in static settings.Furthermore, we show that in dynamic settings with tight computational time budget, our modified greedy yields significant improvements over starting the greedy from scratch, in terms of the solution quality achieved.