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"Handschrift"
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Handwritten digit recognition based on corner detection and convolutional neural network
In the field of inspection and testing, it is necessary to manually fill a large number of test record forms, and the digital information accounts for a large proportion in the record forms. For the purpose of automatic and accurate recognition of handwritten form data, this paper proposes a handwritten digit recognition method based on Harris corner detection algorithm and convolutional neural network(CNN). The Harris corner detection algorithm is used to identify the positioning marks in the form image, which determine the possible fill-in positions of handwritten digits. Through a 14-layer CNN model, and using the ReLu function as the excitation function, the handwritten digit features of the target area in the image are recognized. The experimental results show that the average recognition accuracy rate of this method on the test set can reach 98.14%. So a technical solution for intelligent recognition of record forms is thus provided.
Journal Article
Qur'ans of the Umayyads : a first overview
\"For the first time, the dramatic changes the Qur'anic code underwent during the Umayyad period (660-750 C.E.) are analysed and presented on the basis of a selection of material in good part unpublished. In 'Qur'ans of the Umayyads', François Déroche offers a chronology of the various developments which marked the period, in an approach combining philology, art history, codicology and palaeography. The conclusions he reaches challenge the traditional account about the writing down of the Qur'an and throw a new light on the role of the Umayyads in its handwritten diffusion.\" -- Back cover.
Book
Improvement of MNIST Image Recognition Based on CNN
At present, great progress has been made in the field of image recognition, especially in convolutional neural network. Lenet-5 convolutional neural network has been able to identify handwritten digit MNIST database with high precision. In this paper, experiments show that different activation functions, learning rates and the addition of the Dropout layer in front of the output layer will make the convergence speed different, weaken the influence of the initial parameters on the model, and improve the training accuracy. It is proved that the modified LeNet-5 model has a better improvement in handwritten digit recognition. This method is an efficient recognition method.
Journal Article
High-performance brain-to-text communication via handwriting
Brain–computer interfaces (BCIs) can restore communication to people who have lost the ability to move or speak. So far, a major focus of BCI research has been on restoring gross motor skills, such as reaching and grasping
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or point-and-click typing with a computer cursor
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. However, rapid sequences of highly dexterous behaviours, such as handwriting or touch typing, might enable faster rates of communication. Here we developed an intracortical BCI that decodes attempted handwriting movements from neural activity in the motor cortex and translates it to text in real time, using a recurrent neural network decoding approach. With this BCI, our study participant, whose hand was paralysed from spinal cord injury, achieved typing speeds of 90 characters per minute with 94.1% raw accuracy online, and greater than 99% accuracy offline with a general-purpose autocorrect. To our knowledge, these typing speeds exceed those reported for any other BCI, and are comparable to typical smartphone typing speeds of individuals in the age group of our participant (115 characters per minute)
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. Finally, theoretical considerations explain why temporally complex movements, such as handwriting, may be fundamentally easier to decode than point-to-point movements. Our results open a new approach for BCIs and demonstrate the feasibility of accurately decoding rapid, dexterous movements years after paralysis.
A brain–computer interface enables rapid communication through neural decoding of attempted handwriting movements in a person with paralysis.
Journal Article
HWOBC-A Handwriting Oracle Bone Character Recognition Database
The oracle bone character (OBC) from ancient China is the most famous ancient writing systems around the world. Identifying and deciphering OBCs is one of the most important topics in oracle bone study. In research, one of the challenges is that the literature review usually leads to a huge cost of time and manpower. Therefore, the digitazation of OBC literature through the automatic recognition is the inevitable trend of future development. However, the OBCs in the literature are usually writing characters while the database of handwriting OBC has not yet been presented. In this paper, we establish a handwriting oracle bone character database called HWOBC, containing 83,245 character-level samples which are grouped into 3881-character categories. We also present the performance of several baseline DCNN-based methods, in which Melnyk-Net exhibits the best accuracy of 97.64%. It is anticipated that the publication of this database will facilitate the development of OBC research.
Journal Article
Dysgraphia detection through machine learning
Dysgraphia, a disorder affecting the written expression of symbols and words, negatively impacts the academic results of pupils as well as their overall well-being. The use of automated procedures can make dysgraphia testing available to larger populations, thereby facilitating early intervention for those who need it. In this paper, we employed a machine learning approach to identify handwriting deteriorated by dysgraphia. To achieve this goal, we collected a new handwriting dataset consisting of several handwriting tasks and extracted a broad range of features to capture different aspects of handwriting. These were fed to a machine learning algorithm to predict whether handwriting is affected by dysgraphia. We compared several machine learning algorithms and discovered that the best results were achieved by the adaptive boosting (AdaBoost) algorithm. The results show that machine learning can be used to detect dysgraphia with almost 80% accuracy, even when dealing with a heterogeneous set of subjects differing in age, sex and handedness.
Journal Article
Refining Parkinson’s neurological disorder identification through deep transfer learning
Parkinson’s disease (PD), a multi-system neurodegenerative disorder which affects the brain slowly, is characterized by symptoms such as muscle stiffness, tremor in the limbs and impaired balance, all of which tend to worsen with the passage of time. Available treatments target its symptoms, aiming to improve the quality of life. However, automatic diagnosis at early stages is still a challenging medicine-related task to date, since a patient may have an identical behavior to that of a healthy individual at the very early stage of the disease. Parkinson’s disease detection through handwriting data is a significant classification problem for identification of PD at the infancy stage. In this paper, a PD identification is realized with help of handwriting images that help as one of the earliest indicators for PD. For this purpose, we proposed a deep convolutional neural network classifier with transfer learning and data augmentation techniques to improve the identification. Two approaches like freeze and fine-tuning of transfer learning are investigated using ImageNet and MNIST dataset as source task independently. A trained network achieved 98.28% accuracy using fine-tuning-based approach using ImageNet and PaHaW dataset. Experimental results on benchmark dataset reveal that the proposed approach provides better detection of Parkinson’s disease as compared to state-of-the-art work.
Journal Article