This paper presents an algorithm with low computational complexity for classifying and recognizing characters based on a random sampling and high-dimensional binary spaces for the development of real-time applications. Character classification is performed using uniform random sampling as the feature selection process, subsequently performing encoding as binary strings.Associative memories are commonly used as general classifiers with linear criteria to discriminate between data points. In most classifiers, the ability to efficiently detect class membership depends entirely on the expressiveness of the attributes used to encode the data. Each binary pattern encodes the distinct characteristics of several glyphs. Character features are represented as elements of a high-dimensional binary space, where a criterion of the cluster is defined under the L 1 metric. The reduction in computational complexity is analyzed. The reduction in the number of character features through random sampling techniques makes it feasible to manage all the character information in physical architectures; therefore, this approach might use resources on a hardware platform with integer operators typically implemented at the hardware-register level. Finally, this approach is implemented in a parallel architecture Field-Programmable Gate Array (FPGA) and tested using a Database (DB) of different fonts, including distortions, therein showing that the efficiency is comparable to the other well-known approaches. KEYWORDS associative memory, Lernmatrix, OCR, real time, SteinbuchRecently, character recognition systems have become an essential field of research because of diverse applications, including handwritten character recognition 1,2 for license plate recognition, 3 address and zip code recognition, 4 and handwritten recognition. A typical character recognition system consists of the following components: image acquisition, segmentation, feature extraction, and classification. Various methods have been proposed for recognizing characters, such as Int J Circ Theor Appl. 2018;46: 1723-1732.wileyonlinelibrary.com/journal/cta
The perinatal mortality rate is very high throughout the world. A fetal monitor may be used remotely, and this would tackle the problem of continuous monitoring of high-risk pregnancies. There is evidence that current technology is of low reliability, and, therefore, of low precision to identify fetal health. In medical technological implementation, a safe, efficient, and reliable operation must be guaranteed, and the main problem is that remote fetal monitor gathers just a few samples, so the hypothesis of classical theory is not met. We are proposing an approach that improves the data’s lack of reliability that accompanies the use of a remote fetal monitor. The method refers to how, by using the existing technologies and the initial experimental data, it is possible to apply probabilistic models that are truly representative of each application. This leads to the characterization of properties of the statistics used to generate a representative probabilistic model without the need to consider the hard suppositions. Results show that, for every case study, it was possible to improve estimations of measurement uncertainty. The proposed method is a useful tool to increase the reliability of medical technology, especially for pieces of equipment where a health care professional is not available.
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