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In historical manuscripts, humans can detect handwritten words, lines, and decorations with lightness even if they do not know the language or the script. Yet for automatic processing this task has proven elusive, especially in the case of handwritten documents with complex layouts, which is why semiautomatic methods that integrate the human user into the process are needed. In this paper, we introduce...
Automated reading of historical handwriting is needed to search and browse ancient manuscripts in digital libraries based on their textual content. In this paper, we present a combined system for text localization and transcription in page images. It includes flexible learning-based methods for layout analysis and handwriting recognition, which were developed in the context of the Swiss research project...
Text line segmentation is one of the main parts of document image analysis, it provides crucial information for automated reading, word spotting, alignment between image and transcription, or indexing of documents. Yet it remains an open problem for handwritten historical documents because of complex layouts on the one hand, such as curved and touching text lines, and binarization problems on the...
Spotting keywords in handwritten documents without transcription is a valuable method as it allows one to search, index, and classify such documents. In this paper we show that keyword spotting based on bi-directional Long Short-Term Memory (BLSTM) recurrent neural nets can successfully be applied on online handwritten documents with non-text content. It even works without preprocessing steps such...
For historical documents, available transcriptions typically are inaccurate when compared with the scanned document images. Not only the position of the words and sentences are unknown, but also the correct image transcription may not be matched exactly. An error-tolerant alignment is needed to make the document images amenable to browsing and searching in digital libraries. In this paper, we propose...
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