A Novel Scene Text Recognition Method Based on Deep Learning

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.

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Scene Text Recognition Based on Deep Learning

Lecture Notes in Electrical Engineering - Communications, Signal Processing, and Systems ◽

10.1007/978-981-13-9409-6_133 ◽

2020 ◽

pp. 1136-1143

Author(s):

Yunxue Shao ◽

Yuxin Chen

Keyword(s):

Deep Learning ◽

Text Recognition ◽

Scene Text ◽

Scene Text Recognition

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2D Positional Embedding-based Transformer for Scene Text Recognition

Journal of Computational Vision and Imaging Systems ◽

10.15353/jcvis.v6i1.3533 ◽

2021 ◽

Vol 6 (1) ◽

pp. 1-4

Author(s):

Zobeir Raisi ◽

Mohamed A. Naiel ◽

Paul Fieguth ◽

Steven Wardell ◽

John Zelek

Keyword(s):

Spatial Information ◽

State Of The Art ◽

Image Features ◽

Text Recognition ◽

Recognition Method ◽

One Dimensional ◽

Art Scene ◽

Scene Text ◽

In The Wild ◽

Scene Text Recognition

Recent state-of-the-art scene text recognition methods are primarily based on Recurrent Neural Networks (RNNs), however, these methods require one-dimensional (1D) features and are not designed for recognizing irregular-text instances due to the loss of spatial information present in the original two-dimensional (2D) images. In this paper, we leverage a Transformer-based architecture for recognizing both regular and irregular text-in-the-wild images. The proposed method takes advantage of using a 2D positional encoder with the Transformer architecture to better preserve the spatial information of 2D image features than previous methods. The experiments on popular benchmarks, including the challenging COCO-Text dataset, demonstrate that the proposed scene text recognition method outperformed the state-of-the-art in most cases, especially on irregular-text recognition.

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Multi-granularity Deep Local Representations for Irregular Scene Text Recognition

ACM/IMS Transactions on Data Science ◽

10.1145/3446971 ◽

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.

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Arabic Cursive Text Recognition from Natural Scene Images

Applied Sciences ◽

10.3390/app9020236 ◽

2019 ◽

Vol 9 (2) ◽

pp. 236 ◽

Cited By ~ 6

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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