Helping People with Visual Impairments Gain Access to Graphical Information Through Natural Language: The iGraph System

Ferres, Leo; Parush, Avi; Roberts, Shelley; Lindgaard, Gitte

doi:10.1007/11788713_163

Cited by 37 publications

(36 citation statements)

References 13 publications

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“…Data-to-text systems have been developed in many areas, including weather forecasting [16][17][18][19][20], communicating financial and statistical information [21][22][23], and engineering [24]. Most previous data-to-text systems generated summaries of relatively Ano the r ABG was t aken at around 23:00 .…”

Section: Data-to-textmentioning

confidence: 99%

Automatic generation of natural language nursing shift summaries in neonatal intensive care: BT-Nurse

Hunter¹,

Freer²,

Gatt³

et al. 2012

Artificial Intelligence in Medicine

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Section: Data-to-textmentioning

confidence: 99%

Automatic generation of natural language nursing shift summaries in neonatal intensive care: BT-Nurse

Hunter¹,

Freer²,

Gatt³

et al. 2012

Artificial Intelligence in Medicine

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“…This was probably the first demonstration of this kind for a data-to-text NLG system. A number of other data-to-text systems have been developed to summarise small data sets, including summaries of statistical data [24,41], air quality reports [6], and financial data [18,46].…”

Section: Data-to-text Systemsmentioning

confidence: 99%

Automatic Generation of Textual Summaries from Neonatal Intensive Care Data

Portet

Reiter

Hunter

et al.

Artificial Intelligence in Medicine

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“…This also helps journalists for content writing or in case of finding images related to their article. With this it also increases accessibility of multimedia data on Internet for visually impaired people [17]. In general basically in two steps caption is generated.…”

Section: Introductionmentioning

confidence: 99%

Content based Caption Generation for Images Embedded in News Articles

Kohakade¹,

Emmanuel²

2014

IJCA

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In current digital world Content based Image retrieval is becoming critical problem as size of data on Internet increasing rapidly. When the image is embedded in news article it is retrieved by manipulating words annotated to that image, text placed surrounding to that image etc. Many times this annotation, caption generation is done manually. It reduces accuracy, increases time span and makes it as tough task. We proposed a new approach for generating caption for such images. Approach presented here focuses on important terms occurring in news like named entities, using term weighting find out weighted terms which helps in describing news. On other hand by image processing we find out who's in picture as it helps in making accurate caption by using face recognition and it will increase image retrieval. Some of experiments presented here shows performance of face recognition algorithms on standard datasets and also on own developed face dataset, also we train NER model on Indian names which gives better results. As it covers text and image content it helps in generating better caption and also for improving image retrieval accuracy.

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Helping People with Visual Impairments Gain Access to Graphical Information Through Natural Language: The iGraph System

Cited by 37 publications

References 13 publications

Automatic generation of natural language nursing shift summaries in neonatal intensive care: BT-Nurse

Automatic generation of natural language nursing shift summaries in neonatal intensive care: BT-Nurse

Automatic Generation of Textual Summaries from Neonatal Intensive Care Data

Content based Caption Generation for Images Embedded in News Articles

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