Recognition of historical documents is still an active research field due to the relatively low recognition accuracy achieved when processing old fonts or low-quality images. In this work, we investigate the use of Large Language Models (LLMs) for the correction of the OCR for old Greek documents. We examine two different old Greek datasets, one machine printed and one typewritten, using a Deep Network based OCR together with several known and easy-to-use LLMs for the correction of the result. Additionally, we synthetically produce erroneous texts and change the LLM prompts in order to further study the behavior of LLMs for correcting old Greek noisy text. Experimental results highlight the potential of LLMs for OCR correction of old Greek documents especially for the cases that the recognition results are relatively poor.

Recent research activity for page segmentation and pixel-labeling problems focuses strongly on deep Neural Network architectures. In this paper, we present a Convolutional Encoder-Decoder based method for the segmentation of historical handwritten images into distinct text zones. This is achieved by labeling each pixel of the image to one of the predefined classes (main body, comments, decorations, periphery, background). Traditional methods make use of prior knowledge of documents and rely on data-oriented features and experimental rules. We propose a method using Convolutional Encoder-Decoder pairs and we show that deep architectures fit properly to our problem. Experiments on different public datasets demonstrate the effectiveness of the proposed method that outperforms previous techniques in many cases.

H-DIBCO 2018 is the international Handwritten Document Image Binarization Contest organized in the context of ICFHR 2018 conference. The general objective of the contest is to record recent advances in document image binarization using established evaluation performance measures. This paper describes the contest details including the evaluation measures used as well as the performance of the 8 submitted methods along with a brief description of each method.