Preserving Text Content from Historical Handwritten Documents
We propose a holistic, dynamic method to preserve text content with zero tolerance while removing marginal noise for historical handwritten document images. The key idea is to identify and analyze the region between the sharp peak at the edge and page frame of the text content at each margin. Depending on the proximity of the sharp peak to the text, the text content is then extracted from the document image. This method automatically adapts thresholds for each single document image and is directly applicable to gray-scale images. The proposed method is evaluated on four diverse handwritten historical datasets: Queensland State Archive (QSA), Saint Gall, Parzival and the Prosecution Project. Experimental results show that the proposed method achieves higher accuracy compared with other methods tested on the Saint Gall and Parzival datasets, whilst for the other two Australian datasets, which have been introduced here for the first time, the results are very encouraging.
Proceedings: 12th IAPR International Workshop on Document Analysis Systems (DAS 2016)
Artificial Intelligence and Image Processing not elsewhere classified