Toward implementation of associative model in real time for character recognition: A hardware architecture proposal for embedded systems

Abstract

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

Guest editorial: Special issue on computational image sensors and smart camera hardware

“…Vazquez‐Cervantes et al address embedded character recognition in the article “Toward Implementation of Associative Model in Real Time for Character Recognition: A Hardware Architecture Proposal for Embedded Systems.” They describe not only an algorithmic approach for this task but also how to deal with its physical implementation in an FPGA.…”

Research on Artificial Intelligence Machine Learning Character Recognition Algorithm Based on Feature Fusion

“…Traditional character recognition methods are based on the intuitive morphological characteristics of characters. By statistically analyzing the morphological differences between characters, a set of approximate optimal statistical parameters that can represent the differences of characters can be found to screen and recognize characters, so as to achieve the purpose of computer character recognition and automatic entry and preservation [7]. However, the recognition results are always unsatisfactory, and it is still difficult to obtain a good recognition rate even for English with few character sets.…”