Chinese Character Components Segmentation Method Based on Faster RCNN

This paper attempts to use a delta robot's structure and reliable coordinates to develop a selflearning Chinese calligraphy-writing system that requires precise control. Ideally, to achieve human-like behavior, a delta robot can learn stroke trajectories autonomously and present the stroke beauty of calligraphy characters. Unfortunately, state-of-the-art approaches have not yet considered the presentation of stroke beauty resulting from angles of rotation and tilt of the brush. This paper presents an integrated system consisting of a stroke processing module, a hypothesis generation net (HGN) learning model, a delta robot, and an image capture module. Our approach utilizes both the stroke trajectories from the stroke processing module and angles information from the HGN learning model to automatically produce five degrees of freedom action instructions. Based on the instructions, the delta robot completes calligraphy writing. Then, the image capture module provides feedback to the writing system for error calculation and coordinate correction. We utilize the mean absolute percentage error to verify the performance of the writing results. A correction algorithm and linear regression were used to improve the error correction results (less than 2% error). After several cycles, the written results approached the target sample finally. Consequently, the written results produced by the delta robot prove that our proposed system is capable of self-learning and correction.

show abstract

“…Figs. [16][17][18][19] show the writing results and error rates of the characters "永" and "水" using the self-made delta robot.…”

Section: Experiments and Resultsmentioning

confidence: 99%

A Chinese Calligraphy-Writing Robotic System Based on Image-to-Action Translations and a Hypothesis Generation Net

Hsu

Chien

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…To objectively evaluate the algorithm merits, in addition to the CM-GhostNet-SSD algorithm, five other control groups are set up: the Faster R-CNN algorithm [25], the classical SSD algorithm, the DSSD algorithm [26], the traditional GhostNet-SSD algorithm based on SENet and the modified-GhostNet-SSD algorithm based on ECANet.…”

Section: E Results Analysismentioning

confidence: 99%

Research on Pet Recognition Algorithm With Dual Attention GhostNet-SSD and Edge Devices

Hou

Meng

2022

IEEE Access

View full text Add to dashboard Cite

The ability to identify pets via smart home technology is crucial for managing pet health. However, the present popular object detection method model is too large, support for edge devices is insufficient, and performance is subpar for small object identification in the traditional algorithm, such as SSD. In this study, we propose an improved SSD object detection network algorithm based on a GhostNet network with a dual attention mechanism. A ghost module is introduced to create a lightweight ghost network based on the SSD network. The ghost module is combined with the ECA attention mechanism to dynamically allocate parameters to the target and alter the detection region weights to enhance the model performance. In order to reinforce the model and increase the precision of the small target, the CBAM module was also introduced. It assigns major weights to the output target regions of the large-scale feature layer. According to the experimental findings, the CBAM-GhostNet-SSD network significantly reduces the number of backbone parameters and the calculation amount is reduced by 98.23% when compared to the classic SSD. This lays the groundwork for the self-developed algorithm's successful deployment of edge devices. The predicted rate synchronization increased by 3.1 times, also mAP was 14.5% higher than SSD. The lightweight model quantitative transformation utilized in edge equipment can accurately evaluate the target region and realize dynamic detection. This has a certain guiding significance for the subsequent detection of household pet targets.INDEX TERMS deep learning, object detection, ghostnet, attention modules, edge devices.

show abstract

“…The DCNN network was also utilized to automatically extract Tangut characters, enabling automated script annotation without manual intervention [12]. Other researchers have explored the use of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), as well as their derivatives such as Region-based CNN (RCNN) [13], Faster RCNN [14], and Bidirectional Long Short-Term Memory (Bi-LSTM) [15], for the recognition of more commonly encountered Chinese characters, including standard Chinese [16] and handwritten forms [17]. Furthermore, previous investigations have suggested that wider convolution networks with larger filters and shallower layers may lead to improved performance [18].…”

Section: Previous Studies Have Employed Deep Convolutionalmentioning

confidence: 99%

Minimalist DCT-based Depthwise Separable Convolutional Neural Network Approach for Tangut Script

Prasetiadi,

Saputra,

Ramadhanti

et al. 2023

JournalofDinda.DataScience.IT.DataAnalytics

View full text Add to dashboard Cite

The Tangut script, a lesser-explored dead script comprising numerous characters, has received limited attention in deep learning research, particularly in the field of optical character recognition (OCR). Existing OCR studies primarily focus on widely-used characters like Chinese characters and employ deep convolutional neural networks (CNNs) or combinations with recurrent neural networks (RNNs) to enhance accuracy in character recognition. In contrast, this study takes a counterintuitive approach to develop an OCR model specifically for the Tangut script. We utilize shorter layers with slimmer filters using a depthwise separable convolutional neural network (DSCNN) architecture. Furthermore, we preprocess the dataset using a frequency-based transformation, namely the Discrete Cosine Transform (DCT). The results demonstrate successful training of the model, showcasing faster convergence and higher accuracy compared to traditional deep neural networks commonly used in OCR applications.

show abstract

Chinese Character Components Segmentation Method Based on Faster RCNN

Cited by 7 publications

References 27 publications

A Chinese Calligraphy-Writing Robotic System Based on Image-to-Action Translations and a Hypothesis Generation Net

A Chinese Calligraphy-Writing Robotic System Based on Image-to-Action Translations and a Hypothesis Generation Net

Research on Pet Recognition Algorithm With Dual Attention GhostNet-SSD and Edge Devices

Minimalist DCT-based Depthwise Separable Convolutional Neural Network Approach for Tangut Script

Contact Info

Product

Resources

About