SAFL: A Self-Attention Scene Text Recognizer with Focal Loss

With the aim to solve issues of robot object recognition in complex scenes, this paper proposes an object recognition method based on scene text reading. The proposed method simulates human-like behavior and accurately identifies objects with texts through careful reading. First, deep learning models with high accuracy are adopted to detect and recognize text in multi-view. Second, datasets including 102,000 Chinese and English scene text images and their inverse are generated. The F-measure of text detection is improved by 0.4% and the recognition accuracy is improved by 1.26% because the model is trained by these two datasets. Finally, a robot object recognition method is proposed based on the scene text reading. The robot detects and recognizes texts in the image and then stores the recognition results in a text file. When the user gives the robot a fetching instruction, the robot searches for corresponding keywords from the text files and achieves the confidence of multiple objects in the scene image. Then, the object with the maximum confidence is selected as the target. The results show that the robot can accurately distinguish objects with arbitrary shape and category, and it can effectively solve the problem of object recognition in home environments.

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A Multi-Oriented Scene Text Detection Method Based on Location-Sensitive Segmentation

2020 IEEE 6th International Conference on Computer and Communications (ICCC) ◽

10.1109/iccc51575.2020.9345229 ◽

2020 ◽

Author(s):

Bojun Xia ◽

Zhongyue Chen ◽

Xiaoping Chen

Keyword(s):

Detection Method ◽

Text Detection ◽

Scene Text Detection ◽

Scene Text

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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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