A mobile terminal including a display configured to display information; a short range communication module configured to exchange a signal with an external control device; and a controller configured to receive the signal from the external control device, determine a context at a timing point of receiving the signal, and control an operation corresponding to the signal to be performed in the determined context. Further, the operation includes at least one of activation/deactivation of the display, an activation/deactivation of a lock mode of the mobile terminal, a volume operation and a camera operation.

Mobile terminal and controlling method thereof

We describe Google's online handwriting recognition system that currently supports 22 scripts and 97 languages. The system's focus is on fast, high-accuracy text entry for mobile, touch-enabled devices. We use a combination of state-of-the-art components and combine them with novel additions in a flexible framework. This architecture allows us to easily transfer improvements between languages and scripts. This made it possible to build recognizers for languages that, to the best of our knowledge, are not handled by any other online handwriting recognition system. The approach also enabled us to use the same architecture both on very powerful machines for recognition in the cloud as well as on mobile devices with more limited computational power by changing some of the settings of the system. In this paper we give a general overview of the system architecture and the novel components, such as unified time- and position-based input interpretation, trainable segmentation, minimum-error rate training for feature combination, and a cascade of pruning strategies. We present experimental results for different setups. The system is currently publicly available in several Google products, for example in Google Translate and as an input method for Android devices.

Multi-Language Online Handwriting Recognition

Within the realm of information extraction from documents, detection of tables and charts is particularly needed as they contain a visual summary of the most valuable information contained in a document. For a complete automation of the visual information extraction process from tables and charts, it is necessary to develop techniques that localize them and identify precisely their boundaries. In this paper we aim at solving the table/chart detection task through an approach that combines deep convolutional neural networks, graphical models and saliency concepts. In particular, we propose a saliency-based fully-convolutional neural network performing multi-scale reasoning on visual cues followed by a fully-connected conditional random field (CRF) for localizing tables and charts in digital/digitized documents. Performance analysis, carried out on an extended version of the ICDAR 2013 (with annotated charts as well as tables) dataset, shows that our approach yields promising results, outperforming existing models.

A Saliency-Based Convolutional Neural Network for Table and Chart Detection in Digitized Documents

Text/non-text image classification in the wild with convolutional neural networks

Scene text detection and recognition has become a very active research topic in recent several years. It can find many applications in reality ranging from navigation for vision-impaired people to semantic natural scene understanding. In this survey, we are intended to give a thorough and in-depth reviews on the recent advances on this topic, mainly focusing on the methods that appeared in the past 5 years for text detection and recognition in images and videos, including the recent state-of-the-art techniques on the following three related topics: (1) scene text detection, (2) scene text recognition and (3) end-to-end text recognition system. Compared with the previous survey, this survey pays more attention to the application of deep learning techniques on scene text detection and recognition. We also give a brief introduction of other related works such as script identification, text/non-text classification and text-to-image retrieval. This survey also reviews and summarizes some benchmark datasets that are widely used in the literature. Based on these datasets, performances of state-of-the-art approaches are shown and discussed. Finally, we conclude this survey by pointing out several potential directions on scene text detection and recognition that need to be well explored in the future.

Scene text detection and recognition with advances in deep learning: a survey

https://hal.archives-ouvertes.fr/hal-00933509/document

HBF49 feature set: A first unified baseline for online symbol recognition

A flexible framework for online document segmentation by pairwise stroke distance learning

In this work, we present a new method for discriminating textual from non-textual ink strokes in unconstrained handwritten online documents. A Conditional Random Field is utilized for jointly modeling several sources of information (local, spatial, temporal) that contribute to improve the classification accuracy at the stroke level. Experiments over the publicly available IAM-OnDo database validate the approach with an overall recognition rate of more than 96%, and highlight the contributions of the different sources of contextual information.

/pdf/text-non-text-classification-in-online-handwritten-documents-13yu5gwr3l.pdf

Text/Non-text Classification in Online Handwritten Documents with Conditional Random Fields

In this paper, we introduce the Intuidoc-Loustic Gestures DataBase (ILGDB), a new publicly available database of realistic pen-based gestures for evaluation of recognition systems in pen-enabled interfaces. IL-GDB was collected in a real world context and in an immersive environment. As it contains a large number of unconstrained user-defined gestures, ILGDB offers a unique diversity of content that is likely to serve as a precious tool for benchmarking of gesture recognition systems. We report first baseline experimental results on the task of Writer-Dependent gesture recognition.

https://hal.inria.fr/hal-00802340/document

The ILGDB database of realistic pen-based gestural commands

We present a new system for predicting the segmentation of online handwritten documents into multiple blocks, such as text paragraphs, tables, graphics, or mathematical expressions. A hierarchical representation of the document is adopted by aggregating strokes into blocks, and interactions between different levels are modeled in a tree Conditional Random Field. Features are extracted, and labels are predicted at each tree level with logistic classifiers, and Belief Propagation is adopted for optimal inference over the structure. Being fully trainable, the system is shown to properly handle difficult segmentation problems arising in unconstrained online note-taking documents, where no prior knowledge is available regarding the layout or the expected content. Our experiments show very promising results and allow to envision fully automatic segmentation of free-form online notes.

Multi-class segmentation of free-form online documents with tree conditional random fields

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