iDocChip  A Configurable Hardware Architecture for Historical Document Image Processing: Text Line Extraction Digitizing historical archives poses a great challenge due to the quality degradation existing in these documents. Hence, even wellestablished Optical Character Recognition (OCR) systems, such as Abby, OCRopus, Tesseract, etc., fail to give sufficient recognition accuracy for historical archives, since they are optimized for transcribing contemporary documents. In contrast, the opensource anyOCR system is designed specifically for digitizing historical documents with stateoftheart image processing techniques, to achieve high accuracy. Nowadays, the retrieval of historical document images for further OCR requires special scanning devices that are bulky and stationary. As a result, a portable device that combines scanning and OCR capabilities is beneficial to transcribe documents without the need to remove them from where they are archived. For example, smart goggles equipped with embedded OCR device can be used for instant word spotting. However, the available anyOCR software implementation has long runtime and high power consumption. As a solution, we propose a low power, energyefficient accelerator with realtime capabilities called iDocChip, which is a hybrid hardwaresoftware programmable SystemonChip (SoC) for digitizing historical documents. This chip can be easily integrated in a portable device. This paper focuses on one of the most crucial processing steps in anyOCR: Text line extraction. We propose, to the best of our knowledge, the first hybrid hardwaresoftware architecture of the text line extraction technique implemented on an FPGA based programmable SoC. The resulting custom hardware accelerator outperforms the existing anyOCR software implementation by 120x, while achieving 1700x higher energy efficiency without affecting the high accuracy of the system.

Text Line Segmentation in Historical Document Images Using an Adaptive UNet Architecture On most document image transcription, indexing and retrieval systems, text line segmentation remains one of the most important preliminary task. Hence, the research community working in document image analysis is particularly interested in providing reliable text line segmentation methods. Recently, an increasing interest in using deep learningbased methods has been noted for solving various subfields and tasks related to the issues surrounding document image analysis. Thanks to the computer hardware and software evolution, several methods based on using deep architectures continue to outperform the pattern recognition issues and particularly those related to historical document image analysis. Thus, in this paper we present a novel deep learningbased method for text line segmentation of historical documents. The proposed method is based on using an adaptive UNet architecture. Qualitative and numerical experiments are given using a large number of historical document images collected from the Tunisian national archives and different recent benchmarking datasets provided in the context of ICDAR and ICFHR competitions. Moreover, the results achieved are compared with those obtained using the stateoftheart methods.