Improving OCR Accuracy in Historical Archives with Deep Learning

Historical OCR has long struggled with noisy scans, rare fonts, and degraded texts. Recent research shows that deep learning approaches—like LSTM networks trained on gray-level data, mixed models spanning centuries of typefaces, and CNN-LSTM hybrids—significantly improve recognition accuracy. New datasets, open-source systems like anyOCR, and tools such as Calamari and Tesseract 4 push OCR closer to human-level performance, achieving accuracy rates as high as 98%. Together, these advancements are transforming how historical archives and rare printings are digitized and preserved for the digital age.

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Abstract and 1. Introduction

1.1 Printing Press in Iraq and Iraqi Kurdistan

1.2 Challenges in Historical Documents

1.3 Kurdish Language

2.5 Latin

Vamvakas et al. (2008) presented a complete OCR methodology for recognizing historical documents. It is possible to apply this methodology to both machine-printed and handwritten documents. Due to its ...