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• Presents the process to create a new database of historical tabular manuscripts. • The created database is called HisClima and is freely accessible. • Interactive DLA was used to ease text line detection and table structure creation. • Computer assisted transcription was used to ease the text information acquisition. • Baseline results for DLA, Text Recognition and Probabilistic Indexing are provided. Processing automatically historical document images to allow the search of textual information requires the preparation of ground-truth data for training and evaluation. This process is an expensive and arduous task, especially when the historical document images contain specialized vocabulary and/or tabular information. In the latter case, relevant decisions have to be taken to annotate the tabular parts. This paper presents a complex collection of historical document images and the resulting database, which is called HisClima. In this database, half of the images are in tabular format and half as running text. Both types of images contain pre-printed and handwritten text. The textual information is plenty of abbreviations and specific vocabulary related to weather conditions and old ships. This database can be used to research technologies related to historical document image processing and analysis, both for tabular and running text recognition. Baseline results are presented for Document Layout Analysis, Text Recognition, and Probabilistic Indexing. Although these results are good, there is still room for improvement and some indications are provided in this direction. [ABSTRACT FROM AUTHOR]

Abstract: The transcription of historical documents is one of the most interesting tasks in which Handwritten Text Recognition can be applied, due to its interest in humanities research.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 decoding processes (Viterbi decoding) are employed, which allows a possible combination of the two modalities with little difficulties. In this work, we explore the possibility of using recognition results of one modality to restrict the decoding process of the other modality, and apply this process iteratively. Results of these multimodal iterative alternatives are significantly better than the baseline uni-modal systems and better than the non-iterative alternatives. [Copyright &y& Elsevier]