There is no evidence that mathematical semantics cannot be understood due to blindness, the problem is the current access barrier to mathematical resources. In light of this problem, this survey aims at providing visually impaired persons (VIPs), or people close to them, with an overview of the currently available software tools for approaching mathematical content. These can be categorized into (a) tools for accessing mathematical documents (where VIPs are just consumers of content), and (b) tools that allow VIPs to become the creators of mathematical content and even to execute mathematical operations. We also explain the advantages and disadvantages of several key technologies used to interact with mathematics. Moreover, we discuss the necessity of the most common formats and languages behind these tools. Finally, we outline promising paths for future research and development towards blind-friendly mathematical resources. The authors hope that this survey may encourage researchers to engage with the still unsolved challenges of this topic.

Document layout analysis plays an important role in the area of Document Understanding. It is responsible for identifying and classifying the different components of digital documents. Currently, there is no universal algorithm that fits all types of digital documents. This work presents a novel approach for identifying tables, figures, isolated equations, and text regions in scientific papers using deep learning and computer vision techniques. Our proposed approach is a three-stage system: (i) obtaining the spectrograms of the horizontal and vertical intensity histograms of segmented regions of interest. (ii) labeling segmented regions of interest into text, table, and figures using a deep convolutional neural network classifier. (iii) identifying isolated equations in text regions using Bag of Visual Words (BOVW) with Zernike moments. We built a new dataset composed of 11007 papers to perform the experiments, using two common segmentation metrics to evaluate our model: (1) Adjusted Rand Index (ARI) and (2) Variation of Information (VI). The proposed document layout analysis system reached an overall accuracy of 96.2685%, outperforming prior art with a less computational cost.

Continuous monitoring of active volcanoes is essential for understanding their behavior and providing accurate forecasts and warnings. Automating the detection of volcano microseismic events plays a crucial role in large-scale analysis, early warning systems, and efficient handling of large-scale data. This paper presents a novel approach for the automated detection of microseismic events associated with volcanic activity, focusing on the Cotopaxi volcano in Ecuador. Inspired by voice activity detection (VAD) systems used in speech processing, we propose an Adaptive-Microseismic Activity Detector (A-MAD) that modifies VAD techniques to identify frames within seismic signals containing volcanic activity. The A-MAD system incorporates a spectral subtraction stage to mitigate the impact of environmental noise and employs Gaussian Mixture Models (GMMs) to model the probabilistic distribution of microseismic events. Mel Frequency Cepstral Coefficients (MFCCs) are used as features to describe the seismic signals, enabling adaptability for varying noise levels. Experimental results on signals from Cotopaxi Volcano in Ecuador demonstrate the effectiveness of the A-MAD system, achieving high accuracy (96.39% for discrete events and 98.45% for continuous signals) while meeting the efficiency requirements of volcano monitoring institutions. This work contributes to the advancement of early warning systems for volcanic eruptions, providing a robust and adaptive approach for microseismic activity detection.