The digitization of historical handwritten document images is important for the preservation of cultural heritage. Moreover, the transcription of text images obtained from digitization is necessary to provide efficient information access to the content of these documents. Handwritten Text Recognition (HTR) has become an important research topic in the areas of image and computational language processing that allows us to obtain transcriptions from text images. State-of-the-art HTR systems are, however, far from perfect. One difficulty is that they have to cope with image noise and handwriting variability. Another difficulty is the presence of a large amount of Out-Of-Vocabulary (OOV) words in ancient historical texts. A solution to this problem is to use external lexical resources, but such resources might be scarce or unavailable given the nature and the age of such documents. This work proposes a solution to avoid this limitation. It consists of associating a powerful optical recognition system that will cope with image noise and variability, with a language model based on sub-lexical units that will model OOV words. Such a language modeling approach reduces the size of the lexicon while increasing the lexicon coverage. Experiments are first conducted on the publicly available Rodrigo dataset, which contains the digitization of an ancient Spanish manuscript, with a recognizer based on Hidden Markov Models (HMMs). They show that sub-lexical units outperform word units in terms of Word Error Rate (WER), Character Error Rate (CER) and OOV word accuracy rate. This approach is then applied to deep net classifiers, namely Bi-directional Long-Short Term Memory (BLSTMs) and Convolutional Recurrent Neural Nets (CRNNs). Results show that CRNNs outperform HMMs and BLSTMs, reaching the lowest WER and CER for this image dataset and significantly improving OOV recognition.

Back-of-device (BoD) interaction using current smartphone sensors (e.g. accelerometer, microphone, or gyroscope) has recently emerged as a promising novel input modality. Researchers have used a different number of features derived from these commodity sensors, however it is unclear what sensors and which features would allow for practical use, since not all sensor measurements have an equal value for detecting BoD interactions reliably and efficiently. In this paper, we primarily focus on constructing and selecting a subset of features that is a good predictor of BoD tap-based input while ensuring low energy consumption. As a result, we build several classifiers for a variety of use cases (e.g. single or double taps with the dominant or non-dominant hand). We show that a subset of just 5 features provides high discrimination power and results in high recognition accuracy. We also make our software publicly available, so that others can build upon our work.

Transcription of handwritten documents is an important research topic for multiple applications, such as document classification or information extraction. In the case of historical documents, their transcription allows to preserve cultural heritage because of the amount of historical data contained in those documents. The transcription process can employ state-of-the-art handwritten text recognition systems in order to obtain an initial transcription. This transcription is usually not good enough for the quality standards, but that may speed up the final transcription of the expert. In this framework, the use of collaborative transcription applications (crowdsourcing) has risen in the recent years, but these platforms are mainly limited by the use of non-mobile devices. Thus, the recruiting initiatives get reduced to a smaller set of potential volunteers. In this paper, an alternative that allows the use of mobile devices is presented. The proposal consists of using speech dictation of handwritten text lines. Then, by using multimodal combination of speech and handwritten text images, a draft transcription can be obtained, presenting more quality than that obtained by only using handwritten text recognition. The speech dictation platform is implemented as a mobile device application, which allows for a wider range of population for recruiting volunteers. A real acquisition on the contents of a Spanish historical handwritten book was obtained with the platform. This data was used to perform experiments on the behaviour of the proposed framework. Some experiments were performed to study how to optimise the collaborators effort in terms of number of collaborations, including how many lines and which lines should be selected for the speech dictation.

According to statistics data, the population of many countries is aging. Moreover, there is also a small sector of the population that is affected by disabilities. Researchers should develop systems to improve their quality of life. In recent years, the development of smartphones and reduced size devices, but with high processing capacity, has increased dramatically. We can take profit of the sensors and applications embedded in the smartphones in order to monitor disabled and elderly people. In this paper, we describe a smart collaborative system based on the sensors placed in the mobile devices, which allow us to monitor the status of a person based on what is happening in the group people. The network algorithm and the smart system protocol is described and simulated in order to show the performance of our proposal.