Research on Calligraphy Evaluation Technology Based on Deep Learning

Today, when computer-assisted instruction (CAI) is booming, related research in the field of calligraphy education still hasn’t much progress. This main research for the calligraphy beginners to evaluate their works anytime and anywhere. Author uses the literature research and interview to understand the common writing problems of beginners. Then conducts discussion on these problems, design of solutions, research on algorithms, and experimental verification. Based on the ResNet-50 model, through WeChat applet implements for beginners. The main research contents are as follows: (1) In order to achieve good results in calligraphy judgment, this article uses the ResNet-50 model to judge calligraphy. First, adjust the area of the handwritten calligraphy image as the input of the network to a small block suitable for the network. While training the network, adjust the learning rate, the number of image layers and the number of training samples to achieve the optimal. The research results show that ResNet has certain practicality and reference value in the field of calligraphy judgment. Regarding the possible over-fitting problem, this article proposes to improve the accuracy of the judgment by collecting more data and optimizing the data washing process. (2) Combining the rise of WeChat applets, in view of the current WeChat applet learning platform development process and the problem of fewer functional modules, this paper uses cloud development functions to develop a calligraphy learning platform based on WeChat applets. While simplifying the development process, it ensures that the functional modules of the platform meet the needs of teachers and beginners, it has certain practicality and commercial value. After the development of the calligraphy learning applet is completed, it will be submitted for official

A study of feature extraction for Arabic calligraphy characters recognition

Optical character recognition (OCR) is one of the widely used pattern recognition systems. However, the research on ancient Arabic writing recognition has suffered from a lack of interest for decades, despite the availability of thousands of historical documents. One of the reasons for this lack of interest is the absence of a standard dataset, which is fundamental for building and evaluating an OCR system. In 2022, we published a database of ancient Arabic words as the only public dataset of characters written in Al-Mojawhar Moroccan calligraphy. Therefore, such a database needs to be studied and evaluated. In this paper, we explored the proposed database and investigated the recognition of Al-Mojawhar Arabic characters. We studied feature extraction by using the most popular descriptors used in Arabic OCR. The studied descriptors were associated with different machine learning classifiers to build recognition models and verify their performance. In order to compare the learned and handcrafted features on the proposed dataset, we proposed a deep convolutional neural network for character recognition. Regarding the complexity of the character shapes, the results obtained were very promising, especially by using the convolutional neural network model, which gave the highest accuracy score

Generating Handwritten Chinese Characters using CycleGAN

Handwriting of Chinese has long been an important skill in East Asia. However, automatic generation of handwritten Chinese characters poses a great challenge due to the large number of characters. Various machine learning techniques have been used to recognize Chinese characters, but few works have studied the handwritten Chinese character generation problem, especially with unpaired training data. In this work, we formulate the Chinese handwritten character generation as a problem that learns a mapping from an existing printed font to a personalized handwritten style. We further propose DenseNet CycleGAN to generate Chinese handwritten characters. Our method is applied not only to commonly used Chinese characters but also to calligraphy work with aesthetic values. Furthermore, we propose content accuracy and style discrepancy as the evaluation metrics to assess the quality of the handwritten characters generated. We then use our proposed metrics to evaluate the generated characters from CASIA dataset as well as our newly introduced Lanting calligraphy dataset.Comment: Accepted at WACV 201

Emotion Detection and Classification using Hybrid Feature Selection and Deep Learning Techniques

Image sentiment analysis has gained significant attention due to the increasing availability of user-generated content on various platforms such as social media, e-commerce websites, and online reviews. The core of our approach lies in the deep learning model, which combines the strengths of convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. The CNN component captures local dependencies and learns high-level features, while the LSTM component captures long-term dependencies and maintains contextual information. By fusing these two components, our model effectively captures both local and global context, leading to improved sentiment analysis performance. During the execution first select the context and generate visual feature vector for generation of captions. The EfficientNetB7 model is applied in order to construct the image description for every individual picture. The Attention-based LSTM as well as Gated Recurrent Unit (GRU) greedy method are the two approaches that are utilized in the process of classifying sentiment labels. The proposed research has been categorized into three different phases. In Phase 1 describe various data preprocessing and normalization techniques. It also demonstrates training using RESNET-101 deep learning-based CNN classification algorithm. In Phase 2 extract the various features from the selected context of input image. The context has been selected based on detected objects from the image and generates a visual caption for the entire dataset. The      generated captions are dynamically used for model training as well as testing to both datasets. The EfficientNet module has used for generation of visual context from selected contexts. Finally in phase 3 classification model has built using a Deep Convolutional Neural Network (DCNN). The proposed algorithm classified the entire train and test dataset with different cross- validations such as 5-fold, 10-fold and 15-fold etc. The numerous activation functions are also used for evaluation of the proposed algorithm in different ways. The higher accuracy of the proposed model is 96.20% sigmoid function for 15-fold cross validation

Chinese calligraphy: character style recognition based on full-page document

Calligraphy plays a very important role in the history of China. From ancient times to modern times, the beauty of calligraphy has been passed down to the present. Different calligraphy styles and structures have made calligraphy a beauty and embodiment in the field of writing. However, the recognition of calligraphy style and fonts has always been a blank in the computer field. The structural complexity of different calligraphy also brings a lot of challenges to the recognition technology of computers. In my research, I mainly discussed some of the main recognition techniques and some popular machine learning algorithms in this field for more than 20 years, trying to find a new method of Chinese calligraphy styles recognition and exploring its feasibility. In our research, we searched for research papers 20 years ago. Most of the results are about the content recognition of modern Chinese characters. At first, we analyze the development of Chinese characters and the basic Chinese character theory. In the analysis of the current recognition of Chinese characters (including handwriting online and offline) in the computer field, it is more important to analyze various algorithms and results, and to analyze how to use the experimental data, besides how they construct the data set used for their test. The research on the method of image processing based on Chinese calligraphy works is very limited, and the data collection for calligraphy test is very limited also. The test of dataset that used between different recognition technologies is also very different. However, it has far-reaching significance for inheriting and carrying forward the traditional Chinese culture. It is very necessary to develop and promote the recognition of Chinese characters by means of computer tecnchque. In the current application field, the font recognition of Chinese calligraphy can effectively help the library administrators to identify the problem of the classification of the copybook, thus avoiding the recognition of the calligraphy font which is difficult to perform manually only through subjective experience. In the past 10 years of technology, some techniques for the recognition of single Chinese calligraphy fonts have been given. Most of them are the pre-processing of calligraphy characters, the extraction of stroke primitives, the extraction of style features, and the final classification of machine learning. The probability of the classification of the calligraphy works. Such technical requirements are very large for complex Chinese characters, the result of splitting and recognition is very large, and it is difficult to accurately divide many complex font results. As a result, the recognition rate is low, or the accuracy of recognition of a specific word is high, but the overall font recognition accuracy is low. We understand that Chinese calligraphy is a certain research value. In the field of recognition, many research papers on the analysis of Chinese calligraphy are based on the study of calligraphy and stroke. However, we have proposed a new method for dealing with font recognition. The recognition technology is based on the whole page of the document. It is studied in three steps: the first step is to use Fourier transform and some Chinese calligraphy images and analyze the results. The second is that CNN is based on different data sets to get some results. Finally, we made some improvements to the CNN structure. The experimental results of the thesis show that the full-page documents recognition method proposed can achieve high accuracy with the support of CNN technology, and can effectively identify the different styles of Chinese calligraphy in 5 styles. Compared with the traditional analysis methods, our experimental results show that the method based on the full-page document is feasible, avoiding the cumbersome font segmentation problem. This is more efficient and more accurate