Evaluation and Recognition of Handwritten Chinese Characters Based on Similarities

Zhao, Yuliang; Zhang, Xinyue; Fu, Boya; Zhan, Zhikun; Sun, Hui; Li, Lianjiang; Zhang, Guanglie

doi:10.3390/app12178521

Cited by 7 publications

(2 citation statements)

References 42 publications

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“…Traditional methods mainly include manual inspection combined with computer technology for character recognition, including template matching, feature-based engineering, and other methods [ 11 , 12 ].…”

Section: Related Workmentioning

confidence: 99%

Detection and Recognition of Tilted Characters on Railroad Wagon Wheelsets Based on Deep Learning

Xu,

et al. 2023

Sensors

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The quality of railroad wheelsets is an important guarantee for the safe operation of wagons, and mastering the production information of wheelsets plays a vital role in vehicle scheduling and railroad transportation safety. However, when using objection detection methods to detect the production information of wheelsets, there are situations that affect detection such as character tilting and unfixed position. Therefore, this paper proposes a deep learning-based method for accurately detecting and recognizing tilted character information on railroad wagon wheelsets. It covers three parts. Firstly, we construct a tilted character detection network based on Faster RCNN for generating a wheelset’s character candidate regions. Secondly, we design a tilted character correction network to classify and correct the orientation of flipped characters. Finally, a character recognition network is constructed based on convolutional recurrent neural network (CRNN) to realize the task of recognizing a wheelset’s characters. The result shows that the method can quickly and effectively detect and identify the information of tilted characters on wheelsets in images.

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Section: Related Workmentioning

confidence: 99%

Detection and Recognition of Tilted Characters on Railroad Wagon Wheelsets Based on Deep Learning

Xu,

et al. 2023

Sensors

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“…As a result, deep neural network (DNN) approaches have been proposed to improve segmentation, feature extraction, and classification/recognition problems in shallow machine learning techniques [4]. Using deep learning approaches, offline HTR has been studied and has shown remarkable results for Latin, Arabic, Devanagari, and Chinese scripts, even for multilingual recognition [5][6][7][8][9]. In [10], a gated convolution neural network (Gated CNN) with a bidirectional gated recurrent unit (GRU) was proposed for offline handwritten text recognition using a publicly available dataset called IAM [11].…”

Section: Introductionmentioning

confidence: 99%

Gated Convolution and Stacked Self-Attention Encoder–Decoder-Based Model for Offline Handwritten Ethiopic Text Recognition

Tadesse,

Liu,

2023

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Offline handwritten text recognition (HTR) is a long-standing research project for a wide range of applications, including assisting visually impaired users, humans and robot interactions, and the automatic entry of business documents. However, due to variations in writing styles, visual similarities between different characters, overlap between characters, and source document noise, designing an accurate and flexible HTR system is challenging. The problem becomes serious when the algorithm has a low learning capacity and when the text used is complex and has a lot of characters in the writing system, such as Ethiopic script. In this paper, we propose a new model that recognizes offline handwritten Ethiopic text using a gated convolution and stacked self-attention encoder–decoder network. The proposed model has a feature extraction layer, an encoder layer, and a decoder layer. The feature extraction layer extracts high-dimensional invariant feature maps from the input handwritten image. Using the extracted feature maps, the encoder and decoder layers transcribe the corresponding text. For the training and testing of the proposed model, we prepare an offline handwritten Ethiopic text-line dataset (HETD) with 2800 samples and a handwritten Ethiopic word dataset (HEWD) with 10,540 samples obtained from 250 volunteers. The experiment results of the proposed model on HETD show a 9.17 and 13.11 Character Error Rate (CER) and Word Error Rate (WER), respectively. However, the model on HEWD shows an 8.22 and 9.17 CER and WER, respectively. These results and the prepared datasets will be used as a baseline for future research.

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Quality evaluation methods of handwritten Chinese characters: a comprehensive survey

Chen,

Su,

Song

et al. 2024

Multimedia Systems

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Evaluation and Recognition of Handwritten Chinese Characters Based on Similarities

Cited by 7 publications

References 42 publications

Detection and Recognition of Tilted Characters on Railroad Wagon Wheelsets Based on Deep Learning

Detection and Recognition of Tilted Characters on Railroad Wagon Wheelsets Based on Deep Learning

Gated Convolution and Stacked Self-Attention Encoder–Decoder-Based Model for Offline Handwritten Ethiopic Text Recognition

Quality evaluation methods of handwritten Chinese characters: a comprehensive survey

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