The dependable and efficient identification of Qin seal script characters is pivotal in the discovery, preservation, and inheritance of the distinctive cultural values embodied by these artifacts. This paper uses image histograms of oriented gradients (HOG) features and an SVM model to discuss a character recognition model for identifying partial and blurred Qin seal script characters. The model achieves accurate recognition on a small, imbalanced dataset. Firstly, a dataset of Qin seal script image samples is established, and Gaussian filtering is employed to remove image noise. Subsequently, the gamma transformation algorithm adjusts the image brightness and enhances the contrast between font structures and image backgrounds. After a series of preprocessing operations, the oriented gradient histograms (HOG) features are extracted from the images. During model training, different weights are assigned to classes with varying sample quantities to address the issue of class imbalance and improve the model's classification accuracy. Results show that the model achieves an accuracy of 95.30%. This research can help historians quickly identify and extract the text content on newly discovered Qin slip cultural relics, shortening the cycle of building a historical database.
Recognition is one of the basic characteristics of human brain, and also for the living creatures. It is possible to recognize images, persons, or patterns according to their characteristics. This recognition could be done using eyes or dedicated proposed methods. There are numerous applications for pattern recognition such as recognition of printed or handwritten letters, for example reading post addresses automatically and reading documents or check reading in bank.
One of the challenges which faces researchers in character recognition field is the recognition of digits, which are written by hand. This paper describes a classification method for on-line handwrit
... Show MoreFace recognition, emotion recognition represent the important bases for the human machine interaction. To recognize the person’s emotion and face, different algorithms are developed and tested. In this paper, an enhancement face and emotion recognition algorithm is implemented based on deep learning neural networks. Universal database and personal image had been used to test the proposed algorithm. Python language programming had been used to implement the proposed algorithm.
The relationship between the vision of the scenario and the director is represented in the second search for new mechanisms and possibilities that possess the direct effect and synonyms of the vision of the scenario, so he works on investing them in achieving his ideas and visions with more effectiveness and flexibility to achieve creative potentials, without resorting to the installations and expressive structures themselves that have exhausted their meaning, and lost their luster due to Its prevalence and familiarity on the one hand, and its activation on the other hand to achieve modernity at the level of aromas, after the director takes a method or approach that has privacy and exclusivity, and based on the foregoing, getting
... Show MoreSpeech recognition is a very important field that can be used in many applications such as controlling to protect area, banking, transaction over telephone network database access service, voice email, investigations, House controlling and management ... etc. Speech recognition systems can be used in two modes: to identify a particular person or to verify a person’s claimed identity. The family speaker recognition is a modern field in the speaker recognition. Many family speakers have similarity in the characteristics and hard to identify between them. Today, the scope of speech recognition is limited to speech collected from cooperative users in real world office environments and without adverse microphone or channel impairments.
In light of the development in computer science and modern technologies, the impersonation crime rate has increased. Consequently, face recognition technology and biometric systems have been employed for security purposes in a variety of applications including human-computer interaction, surveillance systems, etc. Building an advanced sophisticated model to tackle impersonation-related crimes is essential. This study proposes classification Machine Learning (ML) and Deep Learning (DL) models, utilizing Viola-Jones, Linear Discriminant Analysis (LDA), Mutual Information (MI), and Analysis of Variance (ANOVA) techniques. The two proposed facial classification systems are J48 with LDA feature extraction method as input, and a one-dimen
... Show MoreThis research interested to study the structural configuration in the square kufi script, as the kufi script is one of the oldest Arabic fonts, and this name was named according to the city of Kufa, so its types, forms, and designations were numerous, including the square kufic script, which was characterized by being a very straightforward engineering script based on the principle of squaring in building letters to form an engineering body that governs its construction of a set of organizational relationships and design bases according to an engineering structural system. The researchers identified the problem of his research with the following question: What is the Structural configuration in the square Kufic script?
Therefore, the
Taken the word the word God itself the task when the Muslim calligraphers because of its holiness and majesty and altitude, so take Calligraphers innovate in their design, which represents the images and forms experiencing them prolific artistic output to highlight the aesthetic value through the use of Kufic script which is one of the most prominent lines his susceptibility diversity decorative Add the possibility of extending the letters in different directions because of the vision calligrapher aesthetic and an investigation is required for the word of the design, so the researcher examined by dividing into four chapters,Was the first research problem and the importance and goals and identify the term, while the second chapter was div
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