A hypothesize-and-verify framework for Text Recognition using Deep Recurrent Neural Networks

Deep LSTM is an ideal candidate for text recognition. However text recognition involves some initial image processing steps like segmentation of lines and words which can induce error to the recognition system. Without segmentation, learning very long range context is difficult and becomes computationally intractable. Therefore, alternative soft decisions are needed at the pre-processing level. This paper proposes a hybrid text recognizer using a deep recurrent neural network with multiple layers of abstraction and long range context along with a language model to verify the performance of the deep neural network. In this paper we construct a multi-hypotheses tree architecture with candidate segments of line sequences from different segmentation algorithms at its different branches. The deep neural network is trained on perfectly segmented data and tests each of the candidate segments, generating unicode sequences. In the verification step, these unicode sequences are validated using a sub-string match with the language model and best first search is used to find the best possible combination of alternative hypothesis from the tree structure. Thus the verification framework using language models eliminates wrong segmentation outputs and filters recognition errors

The language data repository: machine readable storage for spoken language data

Due to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to [email protected], referencing the URI of the item.Includes bibliographical references (leaf 48).The Language Data Repository project is working to develop a software architecture capable of storing the transcripts and recordings of spoken language data and capable of hosting software tools to aid in the analysis of that data. The proposed software architecture can be used by multiple people to store linguistic data from multiple languages on either local machines or non-local machines that can be accessed via a network by multiple users simultaneously. The primary user community for the LDR software comes from a targeted subset of linguists conducting research on language groups with no officially established or standardized writing system. These linguistic field workers are typically involved in activities such as: learning these "unwritten" languages, developing orthographic systems, beginning literacy programs, and producing written texts in the new orthographic system (e.g., Bible translations and traditional stories). The secondary user community consists of linguists who need a reliable method of storing spoken language data and the transcripts of those data, regardless of the existence of an established or standardized written code for that language. Such a software system offers two main improvements over current, paper-based methods of recording transcripts of linguistic data. First, by utilizing machine-readable storage, it will enable linguists to use computational tools to aid in linguistic analysis by increasing the ability to quickly and accurately test and evaluate linguistic hypotheses of the rules governing the linguistic systems. Secondly, a standardized method of recording data in a machine-readable format will enhance linguists' ability to document their research and share their results with a greater number of colleagues than previously possible. A benefit to this increase in the distribution of primary data to other colleagues is the ability for mote people to test various hypotheses simultaneously

Space for Two to Think: Large, High-Resolution Displays for Co-located Collaborative Sensemaking

Large, high-resolution displays carry the potential to enhance single display groupware collaborative sensemaking for intelligence analysis tasks by providing space for common ground to develop, but it is up to the visual analytics tools to utilize this space effectively. In an exploratory study, we compared two tools (Jigsaw and a document viewer), which were adapted to support multiple input devices, to observe how the large display space was used in establishing and maintaining common ground during an intelligence analysis scenario using 50 textual documents. We discuss the spatial strategies employed by the pairs of participants, which were largely dependent on tool type (data-centric or function-centric), as well as how different visual analytics tools used collaboratively on large, high-resolution displays impact common ground in both process and solution. Using these findings, we suggest design considerations to enable future co-located collaborative sensemaking tools to take advantage of the benefits of collaborating on large, high-resolution displays

Spoken content retrieval: A survey of techniques and technologies

Speech media, that is, digital audio and video containing spoken content, has blossomed in recent years. Large collections are accruing on the Internet as well as in private and enterprise settings. This growth has motivated extensive research on techniques and technologies that facilitate reliable indexing and retrieval. Spoken content retrieval (SCR) requires the combination of audio and speech processing technologies with methods from information retrieval (IR). SCR research initially investigated planned speech structured in document-like units, but has subsequently shifted focus to more informal spoken content produced spontaneously, outside of the studio and in conversational settings. This survey provides an overview of the field of SCR encompassing component technologies, the relationship of SCR to text IR and automatic speech recognition and user interaction issues. It is aimed at researchers with backgrounds in speech technology or IR who are seeking deeper insight on how these fields are integrated to support research and development, thus addressing the core challenges of SCR

Increased flood frequency and magnitude decreases density of a stream-breeding salamander in urbanized watersheds

Background/Question/Methods
As urbanization increases across the globe, more ecologists have taken note of its consequences to stream systems. Sufficient data have been collected to document repeated patterns in urbanized streams for many abiotic parameters, aquatic insects, and fish. For example, we now know that urbanized streams experience more frequent and intense spates as a result of increased runoff form impervious surfaces in the urban watershed. The spates eventually lead to a more incised and wider stream bed. Such abiotic shifts consistently result in decreased aquatic invertebrate and fish diversity. More recently, stream-breeding salamanders have been observed to decrease in density in urban areas. We monitored the density of southern two-lined salamanders for the duration of two cohorts in 12 streams in western Georgia, USA. We then used path analysis to determine the relationships between land-use change, abiotic shifts in the stream environment, and larval salamander density. 

Results/Conclusions
We found that southern two-lined salamanders in the streams we monitored exhibited no change in reproductive output between urban and reference habitats. However, repeated sampling throughout the larval period revealed a large decline in density of larvae in urban areas prior to metamorphosis, while a similar decline was not seen in reference habitats. We evaluated several hypotheses that might explain the observed decline in urban areas, and a model in which increased impervious surface causes an increase in spate frequency and magnitude, which then leads to decreased larval density had the most support. This is the first attempt to compare multiple plausible hypotheses as to why salamander density and diversity decreases in urban habitats. By describing larval density at the beginning and end of the larval period, and by identifying a likely mechanism for the observed decline in density, species-specific and stream restoration efforts can be enhanced.
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One Homonym per Translation

The study of homonymy is vital to resolving fundamental problems in lexical semantics. In this paper, we propose four hypotheses that characterize the unique behavior of homonyms in the context of translations, discourses, collocations, and sense clusters. We present a new annotated homonym resource that allows us to test our hypotheses on existing WSD resources. The results of the experiments provide strong empirical evidence for the hypotheses. This study represents a step towards a computational method for distinguishing between homonymy and polysemy, and constructing a definitive inventory of coarse-grained senses.Comment: 8 pages, including reference