Text Classification Algorithms: A Survey Kowsari, Kamran; Jafari Meimandi, Kiana; Heidarysafa, Mojtaba ...
Information (Basel),
04/2019, Letnik:
10, Številka:
4
Journal Article
Recenzirano
Odprti dostop
In recent years, there has been an exponential growth in the number of complex documents and texts that require a deeper understanding of machine learning methods to be able to accurately classify ...texts in many applications. Many machine learning approaches have achieved surpassing results in natural language processing. The success of these learning algorithms relies on their capacity to understand complex models and non-linear relationships within data. However, finding suitable structures, architectures, and techniques for text classification is a challenge for researchers. In this paper, a brief overview of text classification algorithms is discussed. This overview covers different text feature extractions, dimensionality reduction methods, existing algorithms and techniques, and evaluations methods. Finally, the limitations of each technique and their application in real-world problems are discussed.
Scene text detection is an important step of scene text recognition system and also a challenging problem. Different from general object detections, the main challenges of scene text detection lie on ...arbitrary orientations, small sizes, and significantly variant aspect ratios of text in natural images. In this paper, we present an end-to-end trainable fast scene text detector, named TextBoxes++, which detects arbitrary-oriented scene text with both high accuracy and efficiency in a single network forward pass. No post-processing other than efficient non-maximum suppression is involved. We have evaluated the proposed TextBoxes++ on four public data sets. In all experiments, TextBoxes++ outperforms competing methods in terms of text localization accuracy and runtime. More specifically, TextBoxes++ achieves an f-measure of 0.817 at 11.6 frames/s for 1024 × 1024 ICDAR 2015 incidental text images and an f-measure of 0.5591 at 19.8 frames/s for 768 × 768 COCO-Text images. Furthermore, combined with a text recognizer, TextBoxes++ significantly outperforms the state-of-the-art approaches for word spotting and end-to-end text recognition tasks on popular benchmarks. Code is available at: https://github.com/MhLiao/TextBoxes_plusplus.
In this work we present an end-to-end system for text spotting—localising and recognising text in natural scene images—and text based image retrieval. This system is based on a region proposal ...mechanism for detection and deep convolutional neural networks for recognition. Our pipeline uses a novel combination of complementary proposal generation techniques to ensure high recall, and a fast subsequent filtering stage for improving precision. For the recognition and ranking of proposals, we train very large convolutional neural networks to perform word recognition on the whole proposal region at the same time, departing from the character classifier based systems of the past. These networks are trained solely on data produced by a synthetic text generation engine, requiring no human labelled data. Analysing the stages of our pipeline, we show state-of-the-art performance throughout. We perform rigorous experiments across a number of standard end-to-end text spotting benchmarks and text-based image retrieval datasets, showing a large improvement over all previous methods. Finally, we demonstrate a real-world application of our text spotting system to allow thousands of hours of news footage to be instantly searchable via a text query.
Individuals with complex communication needs who use Augmentative and Alternative Communication (AAC) frequently encounter barriers that limit their ability to achieve their full potential in ...communication and in life. These barriers include access barriers (limitations in the current capabilities of the AAC user or the communication systems that they use) as well as opportunity barriers (e.g., policy, practice, knowledge/skill, and attitude barriers that extend beyond the AAC user). It is essential to consider both access and opportunity barriers when designing systems and supports for individuals who use AAC. However, often the emphasis of research and practice is on addressing issues related to access barriers with far less attention to opportunity barriers. Supporting Individuals Who Use Augmentative and Alternative Communication: Breaking Down Opportunity Barriers is the first book to focus specifically on practical strategies for breaking down opportunity barriers experienced by individuals who use AAC.
We investigated the roles of classroom supports for multiple motivations and engagement in students' informational text comprehension, motivation, and engagement. A composite of classroom contextual ...variables consisting of instructional support for choice, importance, collaboration, and competence, accompanied by cognitive scaffolding for informational text comprehension, was provided in four-week instructional units for 615 grade 7 students. These classroom motivational-engagement supports were implemented within integrated literacy/history instruction in the Concept-Oriented Reading Instruction (CORI) framework. CORI increased informational text comprehension compared with traditional instruction (TI) in a switching replications experimental design. Students' perceptions of the motivational-engagement supports were associated with increases in students' intrinsic motivation, value, perceived competence, and increased positive engagement (dedication) more markedly in CORI than in TI, according to multiple regression analyses. Results extended the evidence for the effectiveness of CORI to literacy/history subject matter and informational text comprehension among middle school students. The experimental effects in classroom contexts confirmed effects from task-specific, situated experimental studies in the literature.
•Classifications and applications of text summarization systems are demonstrated.•Automatic text summarization approaches and their methods are illustrated.•Techniques (building blocks) to implement ...text summarization systems are exhibited.•Standard datasets and text summarization evaluation methods are explored.•Future research directions for automatic text summarization are presented.
Automatic Text Summarization (ATS) is becoming much more important because of the huge amount of textual content that grows exponentially on the Internet and the various archives of news articles, scientific papers, legal documents, etc. Manual text summarization consumes a lot of time, effort, cost, and even becomes impractical with the gigantic amount of textual content. Researchers have been trying to improve ATS techniques since the 1950s. ATS approaches are either extractive, abstractive, or hybrid. The extractive approach selects the most important sentences in the input document(s) then concatenates them to form the summary. The abstractive approach represents the input document(s) in an intermediate representation then generates the summary with sentences that are different than the original sentences. The hybrid approach combines both the extractive and abstractive approaches. Despite all the proposed methods, the generated summaries are still far away from the human-generated summaries. Most researches focus on the extractive approach. It is required to focus more on the abstractive and hybrid approaches. This research provides a comprehensive survey for the researchers by presenting the different aspects of ATS: approaches, methods, building blocks, techniques, datasets, evaluation methods, and future research directions.
•A novel bottom-up method for detecting dense and arbitrary-shaped scene texts.•Explicitly learning repulsive links between close texts helps to separate dense text instances.•Instance-aware loss for ...bottom-up deep learning-based methods, further boosting the performance.•A dataset consisting of dense and arbitrary-shaped scene text of commodity images is introduced.•Significantly improved performance on dense and curved text detection.
State-of-the-art methods have achieved impressive performances on multi-oriented text detection. Yet, they usually have difficulty in handling curved and dense texts, which are common in commodity images. In this paper, we propose a network for detecting dense and arbitrary-shaped scene text by instance-aware component grouping (ICG), which is a flexible bottom-up method. To address the difficulty in separating dense text instances faced by most bottom-up methods, we propose attractive and repulsive link between text components which forces the network learning to focus more on close text instances, and instance-aware loss that fully exploits context to supervise the network. The final text detection is achieved by a modified minimum spanning tree (MST) algorithm based on the learned attractive and repulsive links. To demonstrate the effectiveness of the proposed method, we introduce a dense and arbitrary-shaped scene text dataset composed of commodity images (DAST1500). Experimental results show that the proposed ICG significantly outperforms state-of-the-art methods on DAST1500 and two curved text datasets: Total-Text and CTW1500, and also achieves very competitive performance on two multi-oriented datasets: ICDAR15 (at 7.1FPS for 1280 × 768 image) and MTWI.
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Detecting dense text in natural images Jiang, Dianzhuan; Zhang, Shengsheng; Huang, Yaping ...
IET computer vision,
12/2020, Letnik:
14, Številka:
8
Journal Article
Recenzirano
Odprti dostop
Most existing text detection methods are mainly motivated by deep learning-based object detection approaches, which may result in serious overlapping between detected text lines, especially in dense ...text scenarios. It is because text boxes are not commonly overlapped, as different from general objects in natural scenes. Moreover, text detection requires higher localisation accuracy than object detection. To tackle these problems, the authors propose a novel dense text detection network (DTDN) to localise tighter text lines without overlapping. Their main novelties are: (i) propose an intersection-over-union overlap loss, which considers correlations between one anchor and GT boxes and measures how many text areas one anchor contains, (ii) propose a novel anchor sample selection strategy, named CMax-OMin, to select tighter positive samples for training. CMax-OMin strategy not only considers whether an anchor has the largest overlap with its corresponding GT box (CMax), but also ensures the overlapping between one anchor and other GT boxes as little as possible (OMin). Besides, they train a bounding-box regressor as post-processing to further improve text localisation performance. Experiments on scene text benchmark datasets and their proposed dense text dataset demonstrate that the proposed DTDN achieves competitive performance, especially for dense text scenarios.
Unifying text detection and text recognition in an end-to-end training fashion has become a new trend for reading text in the wild, as these two tasks are highly relevant and complementary. In this ...paper, we investigate the problem of scene text spotting, which aims at simultaneous text detection and recognition in natural images. An end-to-end trainable neural network named as Mask TextSpotter is presented. Different from the previous text spotters that follow the pipeline consisting of a proposal generation network and a sequence-to-sequence recognition network, Mask TextSpotter enjoys a simple and smooth end-to-end learning procedure, in which both detection and recognition can be achieved directly from two-dimensional space via semantic segmentation. Further, a spatial attention module is proposed to enhance the performance and universality. Benefiting from the proposed two-dimensional representation on both detection and recognition, it easily handles text instances of irregular shapes, for instance, curved text. We evaluate it on four English datasets and one multi-language dataset, achieving consistently superior performance over state-of-the-art methods in both detection and end-to-end text recognition tasks. Moreover, we further investigate the recognition module of our method separately, which significantly outperforms state-of-the-art methods on both regular and irregular text datasets for scene text recognition.
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