FACLSTM: ConvLSTM with focused attention for scene text recognition

Wang, Qingqing; Jia, Wei; He, Xiangjian; Lu, Yue; Blumenstein, Michael; Huang, Ye

doi:10.1007/s11432-019-2713-1

Cited by 32 publications

(14 citation statements)

References 29 publications

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“…Compared with regular text recognition, it is much more challenging to recognize irregular text of arbitrary shape for a model. The methods towards irregular scene text recognition usually exploit deep backbone network (e.g., ResNet101 [36]) for image feature extraction, or parallel convolutional layers [37], [39] to learn attention weights, or need per-pixel annotation for supervision [45], [46]. The gauge images utilized in AGC usually appear in regular arrangements, thus the benefits of these methods are limited at a cost of model complexity as well as inference time.…”

Section: A Methods Towards Scene Text Recognitionmentioning

confidence: 99%

PGC-Net: A Light Weight Convolutional Sequence Network for Digital Pressure Gauge Calibration

Lian

et al. 2019

IEEE Access

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Automatic digital pressure gauge calibration is challenging due to various unconstrained conditions. Although existing CNN-RNN based methods have been almost perfect on scene text recognition, they fail to perform well on digital pressure gauge calibration that requires to be extremely computationefficient and accurate. In this paper, we propose a light weight fully convolutional sequence recognition network for fast and accurate digital Pressure Gauge Calibration (PGC-Net). PGC-Net integrates feature extraction, sequence modelling and transcription into a unified framework. Experimental results show that PGC-Net runs 28 fps on CPU with 97.41% accuracy. Compared with previous methods, PGC-Net achieves better or comparable performance at lower inference time. Without bells and whistles, PGC-Net is capable of recognizing decimal points that usually appear in pressure gauge images, which evidently verifies the feasibility of PGC-Net. We collected a dataset that contains 17, 240 gauge images with annotated labels for automatic digital pressure gauge calibration. The dataset has been public for future research.

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Section: A Methods Towards Scene Text Recognitionmentioning

confidence: 99%

PGC-Net: A Light Weight Convolutional Sequence Network for Digital Pressure Gauge Calibration

Lian

et al. 2019

IEEE Access

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“…For identifying corrupted characters from the Character-Aware Neural Network (Char-Net) is presented [69]. The focus attention convolution LSTM (FACLSTM) for text recognition [70] was suggested after considering scene text recognition as a spatial-temporal prediction issue. A dynamic log-polar transformer and a sequence recognition network are combined to build a novel scale-adaptive orientation attention network for recognizing the randomly aligned text in an image [71].…”

Section: Text Recognition Using Deep Learningmentioning

confidence: 99%

ETDR: An Exploratory View of Text Detection and Recognition in Images and Videos

Lokkondra¹,

Ramegowda²,

Thimmaiah³

et al. 2021

RIA

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Images and videos with text content are a direct source of information. Today, there is a high need for image and video data that can be intelligently analyzed. A growing number of researchers are focusing on text identification, making it a hot issue in machine vision research. Since this opens the way, several real-time-based applications such as text detection, localization, and tracking have become more prevalent in text analysis systems. To find out more about how text information may be extracted, have a look at our survey. This study presents a trustworthy dataset for text identification in images and videos at first. The second part of the article details the numerous text formats, both in images and video. Third, the process flow for extracting information from the text and the existing machine learning and deep learning techniques used to train the model was described. Fourth, explain assessment measures that are used to validate the model. Finally, it integrates the uses and difficulties of text extraction across a wide range of fields. Difficulties focus on the most frequent challenges faced in the actual world, such as capturing techniques, lightning, and environmental conditions. Images and videos have evolved into valuable sources of data. The text inside the images and video provides a massive quantity of facts and statistics. However, such data is not easy to access. This exploratory view provides easier and more accurate mathematical modeling and evaluation techniques to retrieve the text in image and video into an accessible form.

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“…Text recognition in natural scenes is widely used, and it has a wide range of applications in the current instant translation of photos, image retrieval and other aspects, not only the above mentioned several algorithms, but also based on RARE [14] Network, FAN [15] Network, FACLSTM [16] Network and other text recognition algorithms.…”

Section: Research On Text Recognitionmentioning

confidence: 99%

Design and Implementation of Sensitive Information Detection Algorithm Based on Deep Learning

Jianchao

2021

The 2021 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy

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With the continuous progress of science and technology, people get information on the Internet more and more quickly, the way is more and more convenient. People are more willing to use mobile devices, computers and other electronic products than to obtain information from paper books, newspapers and other channels. While using these electronic products, there will be many behaviors of storing or spreading bad information. Traditional OCR technology can be widely used in the detection and recognition of ordinary single background level text detection and recognition, but for the natural scene pictures with a certain Angle of the text detection and recognition effect is not good. With the development of deep learning technology, text detection and text recognition are mostly completed by deep learning architecture. In this paper, the popular EAST algorithm and CRNN algorithm are combined to detect and identify the text information in the natural scene.

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FACLSTM: ConvLSTM with focused attention for scene text recognition

Cited by 32 publications

References 29 publications

PGC-Net: A Light Weight Convolutional Sequence Network for Digital Pressure Gauge Calibration

PGC-Net: A Light Weight Convolutional Sequence Network for Digital Pressure Gauge Calibration

ETDR: An Exploratory View of Text Detection and Recognition in Images and Videos

Design and Implementation of Sensitive Information Detection Algorithm Based on Deep Learning

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