<i>i</i>DocChip: A Configurable Hardware Accelerator for an End-to-End Historical Document Image Processing

In recent years, there has been an increasing demand to digitize and electronically access historical records. Optical character recognition (OCR) is typically applied to scanned historical archives to transcribe them from document images into machine-readable texts. Many libraries offer special sta...

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Bibliographic Details
Main Authors: Menbere Kina Tekleyohannes, Vladimir Rybalkin, Muhammad Mohsin Ghaffar, Javier Alejandro Varela, Norbert Wehn, Andreas Dengel
Format: Article
Language:English
Published: MDPI AG 2021-09-01
Series:Journal of Imaging
Subjects:
Online Access:https://www.mdpi.com/2313-433X/7/9/175
Description
Summary:In recent years, there has been an increasing demand to digitize and electronically access historical records. Optical character recognition (OCR) is typically applied to scanned historical archives to transcribe them from document images into machine-readable texts. Many libraries offer special stationary equipment for scanning historical documents. However, to digitize these records without removing them from where they are archived, portable devices that combine scanning and OCR capabilities are required. An existing end-to-end OCR software called anyOCR achieves high recognition accuracy for historical documents. However, it is unsuitable for portable devices, as it exhibits high computational complexity resulting in long runtime and high power consumption. Therefore, we have designed and implemented a configurable hardware-software programmable SoC called <i>i</i>DocChip that makes use of anyOCR techniques to achieve high accuracy. As a low-power and energy-efficient system with real-time capabilities, the <i>i</i>DocChip delivers the required portability. In this paper, we present the hybrid CPU-FPGA architecture of <i>i</i>DocChip along with the optimized software implementations of the anyOCR. We demonstrate our results on multiple platforms with respect to runtime and power consumption. The <i>i</i>DocChip system outperforms the existing anyOCR by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>44</mn><mo>×</mo></mrow></semantics></math></inline-formula> while achieving <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>2201</mn><mo>×</mo></mrow></semantics></math></inline-formula> higher energy efficiency and a <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>3.8</mn><mo>%</mo></mrow></semantics></math></inline-formula> increase in recognition accuracy.
ISSN:2313-433X