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To transcribe historical archives into a machine-readable form, first, the documents are scanned, then an$$\\hbox {OCR}$$<\/jats:tex-math>OCR<\/mml:mtext><\/mml:math><\/jats:alternatives><\/jats:inline-formula>is applied. In order to digitize documents without the need to remove them from where they are archived, it is valuable to have a portable device that combines scanning and$$\\hbox {OCR}$$<\/jats:tex-math>OCR<\/mml:mtext><\/mml:math><\/jats:alternatives><\/jats:inline-formula>capabilities. Nowadays, there exist many commercial and open-source document digitization techniques, which are optimized for contemporary documents. However, they fail to give sufficient text recognition accuracy for transcribing historical documents due to the severe quality degradation\u00a0of such documents. On the contrary, the anyOCR system, which is designed to mainly digitize historical documents, provides high accuracy. However, this comes at a cost of high computational complexity resulting in long runtime and high power consumption. To tackle these challenges, we propose a low power energy-efficient accelerator with real-time capabilities called iDocChip, which is a configurable hybrid hardware-software programmable$$\\hbox {System-on-Chip (SoC)}$$<\/jats:tex-math>System-on-Chip (SoC)<\/mml:mtext><\/mml:mrow><\/mml:math><\/jats:alternatives><\/jats:inline-formula>based on anyOCR\u00a0for digitizing historical documents. In this paper, we focus on one of the most crucial processing steps in the anyOCR system:Text and Image Segmentation<\/jats:italic>, which makes use of a multi-resolution morphology-based algorithm. Moreover, an optimized$$\\hbox {FPGA}$$<\/jats:tex-math>FPGA<\/mml:mtext><\/mml:math><\/jats:alternatives><\/jats:inline-formula>-based hybrid architecture of this anyOCR step along with its\u00a0optimized software implementations are presented. We demonstrate our results on multiple embedded and general-purpose platforms with respect to runtime and power consumption. The resulting hardware accelerator outperforms the existing anyOCR by6.2<\/jats:italic><\/jats:bold>$$\\times$$<\/jats:tex-math>\u00d7<\/mml:mo><\/mml:math><\/jats:alternatives><\/jats:inline-formula>, while achieving207<\/jats:italic><\/jats:bold>$$\\times$$<\/jats:tex-math>\u00d7<\/mml:mo><\/mml:math><\/jats:alternatives><\/jats:inline-formula>higher energy-efficiency and maintaining\u00a0its high accuracy.<\/jats:p>","DOI":"10.1007\/s10766-020-00690-y","type":"journal-article","created":{"date-parts":[[2021,1,30]],"date-time":"2021-01-30T20:02:41Z","timestamp":1612036961000},"page":"253-284","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["iDocChip: A Configurable Hardware Architecture for Historical Document Image Processing"],"prefix":"10.1007","volume":"49","author":[{"given":"Menbere Kina","family":"Tekleyohannes","sequence":"first","affiliation":[]},{"given":"Vladimir","family":"Rybalkin","sequence":"additional","affiliation":[]},{"given":"Muhammad Mohsin","family":"Ghaffar","sequence":"additional","affiliation":[]},{"given":"Javier Alejandro","family":"Varela","sequence":"additional","affiliation":[]},{"given":"Norbert","family":"Wehn","sequence":"additional","affiliation":[]},{"given":"Andreas","family":"Dengel","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,1,30]]},"reference":[{"key":"690_CR1","unstructured":"ABBYY. https:\/\/www.abbyy.com\/en-eu\/. 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