scholarly journals Scene Text Recognition Based on Bidirectional LSTM and Deep Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
MVV Prasad Kantipudi ◽  
Sandeep Kumar ◽  
Ashish Kumar Jha
Keyword(s):  
Deep Learning ◽  
Poor Performance ◽  
Input Image ◽  
Scene Recognition ◽  
Text Recognition ◽  
Novel Approach ◽  
Accuracy And Precision ◽  
Scene Text ◽  
Bidirectional Lstm ◽  
Scene Text Recognition

Deep learning is a subfield of artificial intelligence that allows the computer to adopt and learn some new rules. Deep learning algorithms can identify images, objects, observations, texts, and other structures. In recent years, scene text recognition has inspired many researchers from the computer vision community, and still, it needs improvement because of the poor performance of existing scene recognition algorithms. This research paper proposed a novel approach for scene text recognition that integrates bidirectional LSTM and deep convolution neural networks. In the proposed method, first, the contour of the image is identified and then it is fed into the CNN. CNN is used to generate the ordered sequence of the features from the contoured image. The sequence of features is now coded using the Bi-LSTM. Bi-LSTM is a handy tool for extracting the features from the sequence of words. Hence, this paper combines the two powerful mechanisms for extracting the features from the image, and contour-based input image makes the recognition process faster, which makes this technique better compared to existing methods. The results of the proposed methodology are evaluated on MSRATD 50 dataset, SVHN dataset, vehicle number plate dataset, SVT dataset, and random datasets, and the accuracy is 95.22%, 92.25%, 96.69%, 94.58%, and 98.12%, respectively. According to quantitative and qualitative analysis, this approach is more promising in terms of accuracy and precision rate.

scholarly journals A Novel Scene Text Recognition Method Based on Deep Learning

2019 ◽  
Vol 60 (2) ◽  
pp. 781-794 ◽  
Author(s):  
Maosen Wang ◽  
Shaozhang Niu ◽  
Zhenguang Gao
Keyword(s):  
Deep Learning ◽  
Text Recognition ◽  
Recognition Method ◽  
Scene Text ◽  
Scene Text Recognition

scholarly journals Decoupled Attention Network for Text Recognition

2020 ◽  
Vol 34 (07) ◽  
pp. 12216-12224 ◽  
Author(s):  
Tianwei Wang ◽  
Yuanzhi Zhu ◽  
Lianwen Jin ◽  
Canjie Luo ◽  
Xiaoxue Chen ◽  
...  
Keyword(s):  
Input Image ◽  
Text Recognition ◽  
Visual Features ◽  
Attention Network ◽  
Considerable Research ◽  
Handwritten Text ◽  
Handwritten Text Recognition ◽  
Scene Text ◽  
Alignment Problem ◽  
Scene Text Recognition

Text recognition has attracted considerable research interests because of its various applications. The cutting-edge text recognition methods are based on attention mechanisms. However, most of attention methods usually suffer from serious alignment problem due to its recurrency alignment operation, where the alignment relies on historical decoding results. To remedy this issue, we propose a decoupled attention network (DAN), which decouples the alignment operation from using historical decoding results. DAN is an effective, flexible and robust end-to-end text recognizer, which consists of three components: 1) a feature encoder that extracts visual features from the input image; 2) a convolutional alignment module that performs the alignment operation based on visual features from the encoder; and 3) a decoupled text decoder that makes final prediction by jointly using the feature map and attention maps. Experimental results show that DAN achieves state-of-the-art performance on multiple text recognition tasks, including offline handwritten text recognition and regular/irregular scene text recognition. Codes will be released.1

scholarly journals Multi-granularity Deep Local Representations for Irregular Scene Text Recognition

2021 ◽  
Vol 2 (2) ◽  
pp. 1-18
Author(s):  
Hongchao Gao ◽  
Yujia Li ◽  
Jiao Dai ◽  
Xi Wang ◽  
Jizhong Han ◽  
...  
Keyword(s):  
State Of The Art ◽  
Visual Representation ◽  
Text Recognition ◽  
Natural Scene ◽  
Attention Network ◽  
Training Time ◽  
Scene Text ◽  
Benchmark Datasets ◽  
Local Representations ◽  
Scene Text Recognition

Recognizing irregular text from natural scene images is challenging due to the unconstrained appearance of text, such as curvature, orientation, and distortion. Recent recognition networks regard this task as a text sequence labeling problem and most networks capture the sequence only from a single-granularity visual representation, which to some extent limits the performance of recognition. In this article, we propose a hierarchical attention network to capture multi-granularity deep local representations for recognizing irregular scene text. It consists of several hierarchical attention blocks, and each block contains a Local Visual Representation Module (LVRM) and a Decoder Module (DM). Based on the hierarchical attention network, we propose a scene text recognition network. The extensive experiments show that our proposed network achieves the state-of-the-art performance on several benchmark datasets including IIIT-5K, SVT, CUTE, SVT-Perspective, and ICDAR datasets under shorter training time.

scholarly journals Arabic Cursive Text Recognition from Natural Scene Images

2019 ◽  
Vol 9 (2) ◽  
pp. 236 ◽  
Author(s):  
Saad Ahmed ◽  
Saeeda Naz ◽  
Muhammad Razzak ◽  
Rubiyah Yusof
Keyword(s):  
Recognition System ◽  
Document Image ◽  
Text Recognition ◽  
Chinese Script ◽  
Challenging Problem ◽  
Future Directions ◽  
Scene Text ◽  
Comprehensive Survey ◽  
Recognition Systems ◽  
Scene Text Recognition

This paper presents a comprehensive survey on Arabic cursive scene text recognition. The recent years’ publications in this field have witnessed the interest shift of document image analysis researchers from recognition of optical characters to recognition of characters appearing in natural images. Scene text recognition is a challenging problem due to the text having variations in font styles, size, alignment, orientation, reflection, illumination change, blurriness and complex background. Among cursive scripts, Arabic scene text recognition is contemplated as a more challenging problem due to joined writing, same character variations, a large number of ligatures, the number of baselines, etc. Surveys on the Latin and Chinese script-based scene text recognition system can be found, but the Arabic like scene text recognition problem is yet to be addressed in detail. In this manuscript, a description is provided to highlight some of the latest techniques presented for text classification. The presented techniques following a deep learning architecture are equally suitable for the development of Arabic cursive scene text recognition systems. The issues pertaining to text localization and feature extraction are also presented. Moreover, this article emphasizes the importance of having benchmark cursive scene text dataset. Based on the discussion, future directions are outlined, some of which may provide insight about cursive scene text to researchers.

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