An iterative multimodal framework for the transcription of handwritten historical documents
نویسندگان
چکیده
منابع مشابه
An iterative multimodal framework for the transcription of handwritten historical documents
The transcription of historical documents is one of the most interesting tasks in which Handwritten Text Recognition can be applied, due to its interest in humanities research. One alternative for transcribing the ancient manuscripts is the use of speech dictation by using Automatic Speech Recognition techniques. In the two alternatives similar models (Hidden Markov Models and n-grams) and deco...
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Handwritten Text Recognition is a problem that has gained attention in the last years due to the interest in the transcription of historical documents. Handwritten Text Recognition employs models that are similar to those employed in Automatic Speech Recognition (Hidden Markov Models and n-grams). Dictation of the contents of the document is an alternative to text recognition. In this work, we ...
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Huge amounts of handwritten historical documents are being published by digital libraries world wide. However, for these raw digital images to be really useful, they need to be annotated with informative content. State-of-the-art Handwritten Text Recognition (HTR) approaches require an impressive training effort by expert paleographers. Our contribution is a scalable, end-to-end transcription w...
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We describe our work on text-image alignment in context of building a historical document retrieval system. We aim at aligning images of words in handwritten lines with their text transcriptions. The images of handwritten lines are automatically segmented from the scanned pages of historical documents and then manually transcribed. To train automatic routines to detect words in an image of hand...
متن کاملCITlab ARGUS for historical handwritten documents
We describe CITlab’s recognition system for the HTRtS competition attached to the 14. International Conference on Frontiers in Handwriting Recognition, ICFHR 2014. The task comprises the recognition of historical handwritten documents. The core algorithms of our system are based on multidimensional recurrent neural networks (MDRNN) and connectionist temporal classification (CTC). The software m...
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ژورنال
عنوان ژورنال: Pattern Recognition Letters
سال: 2014
ISSN: 0167-8655
DOI: 10.1016/j.patrec.2012.11.007