Named Entity Recogniton (NER) consists of tagging parts of an unstructured text containing particular semantic information. When applied to handwritten documents, it is possible to do it as a two-step approach in which Handwritten Text Recognition (HTR) is performed prior to tagging the automatic transcription. However, it is also possible to do both tasks simultaneously by using an HTR model that learns to output the transcription and the tagging symbols. In this paper, we focus on improving the one-step approach by introducing the auxiliary task of predicting Pyramidal Histograms of Characters (PHOC) in a Convolutional Recurrent Neural Network (CRNN) model. Moreover, given the recent rise of models that digest large amounts of data, we also study the usage of synthetic data to pretrain the proposed architecture. Our experiments show that by pretraining the PHOC-based architecture on synthetic data substantial improvements can be made in both transcription and tagging quality without compromising the computational cost of the decoding step. The resulting model matches the NER performance of the state-of-the-art while keeping its lightweight nature.

The main aim of the Carabela project was to develop and apply techniques that allow textual searching on massive Spanish collections of 15th-19th century manuscripts. The project focused on a relatively small subset of 125 000 images of collections of interest to underwater archaeology. For this type of manuscripts, state-of-the-art automatic transcription techniques, generally fail to achieve usable transcription accuracy. Therefore, rather than insisting in actual transcription, methodologies for probabilistic indexing of handwritten text images have been adopted. This has allowed us to effectively cope with the intrinsically high degree of uncertainty of the text contained in most historical manuscripts, leading to highly effective systems for textual search and retrieval. Carabela has gone one step further by developing new techniques to classify probabilistically indexed, but otherwise untranscribed, text images according to their textual content. These techniques have been successfully used to automatically classify Carabela bundels (each containing hundreds or thousands of pages) according to their “level of risk” of public exposure, in order to control their access and avoid as much as possible the plundering of Spanish underwater heritage.

Document processing comprises different steps depending on the nature of the documents. For text documents, specially for handwritten documents, transcription of their contents is one of the main tasks. Handwritten Text Recognition (HTR) is the process of automatically obtaining the transcription of the content of a handwritten text document. In document processing, the basic unit for the acquisition process is the page image, whilst line image is the basic form for the HTR process. This is a bottle-neck which is holding back the massive industrial document processing. Baseline detection can be used not only to segment page images into line images but also for many other document processing steps. Baseline detection problem can be formulated as a clustering problem over a set of interest points. In this work, we study the use of an automatic baseline detection technique, based on interest point clustering, in Arabic handwritten documents. The experiments reveal that this technique provides promising results for this task.

Handwritten text is generally captured through two main modalities: off-line and on-line. Smart approaches to handwritten text recognition (HTR) may take advantage of both modalities if they are available. This is for instance the case in computer-assisted transcription of text images, where on-line text can be used to interactively correct errors made by a main off-line HTR system. We present here baseline results on the biMod-IAM-PRHLT corpus, which was recently compiled for experimentation with techniques aimed at solving the proposed multi-modal HTR problem, and is being used in one of the official ICPR-2010 contests.

Pen-based interfaces aim at improving the man-machine interaction of many portable systems. While statistical models can be used to learn pen position sequences, they suffer from the huge variability exhibited by the speed of writing. To improve performance, invariance to the writing speed is needed. Trace segmentation is a technique that can be used to normalize the writing speed. This method is controlled by a parameter called resampling distance. A study of the resampling distance is presented here, along with another approximation to the writing speed normalization called "derivatives normalization". The improvement using trace segmentation was 193% relative to the baseline, whilst the improvement using derivatives normalization was 47.3% relative.

Nowadays, the most successful speech recognition systems are based on stochastic finite-state networks (hidden Markov models and n-grams). Speech translation can be accomplished in a similar way as speech recognition. Stochastic finite-state transducers, which are specific stochastic finite-state networks, have proved very adequate for translation modeling. In this work a speech-to-speech translation system, the EuTRANS system, is presented. The acoustic, language and translation models are finite-state networks that are automatically learnt from training samples. This system was assessed in a series of translation experiments from Spanish to English and from Italian to English in an application involving the interaction (by telephone) of a customer with a receptionist at the front-desk of a hotel.

A finite-state, rule-based morphological analyser is presented here, within the framework of machine translation system TAVAL. This morphological analyser introduces specific features which are particularly useful for translation, such as the detection and morphological tagging of word groups that act as a single lexical unit for translation purposes. The case where words in one such group are not strictly contiguous is also covered. A brief description of the Spanish-to-Catalan and Catalan-to-Spanish translation system TAVAL is given in the paper.