Abstract
Text-to-picture systems attempt to facilitate high-level, user-friendly communication between humans and computers while promoting understanding of natural language. These systems interpret a natural language text and transform it into a visual format as pictures or images that are either static or dynamic. In this paper, we aim to identify current difficulties and the main problems faced by prior systems, and in particular, we seek to investigate the feasibility of automatic visualization of Arabic story text through multimedia. Hence, we analyzed a number of well-known text-to-picture systems, tools, and approaches. We showed their constituent steps, such as knowledge extraction, mapping, and image layout, as well as their performance and limitations. We also compared these systems based on a set of criteria, mainly natural language processing, natural language understanding, and input/output modalities. Our survey showed that currently emerging techniques in natural language processing tools and computer vision have made promising advances in analyzing general text and understanding images and videos. Furthermore, important remarks and findings have been deduced from these prior works, which would help in developing an effective text-to-picture system for learning and educational purposes.
Highlights
A text-to-picture system is a system that automatically converts a natural language text into pictures representing the meaning of that text
A key objective of this review is to investigate the feasibility of Bautomatic visualization of Arabic story text through multimedia^ using available tools and resources, the automatic mapping of Arabic text to multimedia using Arabic language processing capabilities, and developing a successful text-to-picture system for educational purposes
We focus on text-to-picture systems, approaches, and tools, which is the simplest form of visualization, and we review those which have only been published in scientific journals and at conferences
Summary
A text-to-picture system is a system that automatically converts a natural language text into pictures representing the meaning of that text. Throughout the last decade, many working text-to-picture systems have been developed. Rada et al [42] proposed a system for the automatic generation of pictorial representations of simple sentences that would use WordNet as a lexical resource for the automatic translation of an input text into pictures. Ustalov [12] developed a text-to-picture system called Utkus for the Russian language. A system to create pictures to illustrate instructions for medical patients was developed by Duy et al [15]. It has a pipeline of five processing phases: pre-processing, medication annotation, post-processing, image construction, and image rendering. It enables users to briefly acquire the patient’s medical data, which is visualized spatially and temporarily based on the categorization of multiple classes consisting of event categories and six physiological systems
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