Memory-Tree Based Design of Optical Character Recognition in FPGA

Abstract

As one of the fields of Artificial Intelligence (AI), Optical Character Recognition (OCR) systems have wide application in both industrial production and daily life. Conventional OCR systems are commonly designed and implement data computation on the basis of microprocessors; the performance of the processor relates to the effect of the computation. However, due to the “Memory-wall” problem and Von Neumann bottlenecks, the drawbacks of traditional processor-based computing for OCR systems are gradually becoming apparent. In this paper, an approach based on the Memory-Centric Computing and “Memory-Tree” algorithm has been proposed to perform hardware optimization of traditional OCR systems. The proposed algorithm was first designed in software implementation using C/C++ and OpenCV to verify the feasibility of the idea and then the RTL conversion of the algorithm was done using the Xilinx Vitis High Level Synthesis (HLS) tool to implement the hardware. This work chose Xilinx Alveo U50 FPGA Accelerator to complete the hardware design, which can be connected to the x86 CPU in the PC by PCIe to form heterogeneous computing. The results of the hardware implementation show that the system this work designed can recognize characters of English capital letters and numbers within 34.24 us. The power of FPGA is 18.59 W, which saves 77.87% of energy consumption compared to the 84 W of the processor in PC.

Sinhala Inscription Character Recognition Model using Deep Learning Technologies

“…In languages like Sinhala, Pali, Tamil, Bengali, and English, there has been a lot of research on optical character recognition in the past few years [11]. According to the information gathered, most of the research has been done on printed and handwritten character recognition.…”

“…Michael Fuchs (2017) [11] has conducted projects with ABBYY Gothic OCR for the automated detection of historical documents. In this paper, he has presented an overview of different elements that play a part in capturing historical documents using OCR technology.…”