Abstract:Offline and online handwriting patterns vary when exposed to external factors. Recent researches on both type handwriting analysis were focused on the image feature extraction, pattern recognition, and classification approach. However, no studies have considered segregating the conditional effects of handwriting patterns on two categories: offline vs. online and normal vs. vibration. Hence, the main goal of the study was to investigate the effects of classifying induced vibration on the offline and online hand… Show more
“…The handwriting entails an offline dataset by manually converting handwritten text from physical paper into digital form by scanning it manually and saving it as a digital image in .jpg format. The data does not contain any online dataset information, which represents image data by digital pen movement with sequential information e.g., position, velocity, and acceleration [ 6 ]. Fig.…”
“…The handwriting entails an offline dataset by manually converting handwritten text from physical paper into digital form by scanning it manually and saving it as a digital image in .jpg format. The data does not contain any online dataset information, which represents image data by digital pen movement with sequential information e.g., position, velocity, and acceleration [ 6 ]. Fig.…”
“…Due to the endless variation in the writing styles of individual writers, it is indeed a challenging task to recognise the written text. However, this can be achieved through feature extraction of images to eliminate non-essential variation and only retain recognition-related data (Wong & Loh, 2019).…”
Arabic handwriting is slightly different from the handwriting of other languages; hence it is possible to distinguish the handwriting written by the native or non-native writer based on their handwriting. However, classifying Arabic handwriting is challenging using traditional text recognition algorithms. Thus, this study evaluated and validated the utilisation of deep transfer learning models to overcome such issues. Hence, seven types of deep learning transfer models, namely the AlexNet, GoogleNet, ResNet18, ResNet50, ResNet101, VGG16, and VGG19, were used to determine the most suitable model for classifying the handwritten images written by the native or non-native. Two datasets comprised of Arabic handwriting images were used to evaluate and validate the newly developed deep learning models used to classify each model’s output as either native or foreign (non-native) writers. The training and validation sets were conducted using both original and augmented datasets. Results showed that the highest accuracy is using the GoogleNet deep learning model for both normal and augmented datasets, with the highest accuracy attained as 93.2% using normal data and 95.5% using augmented data in classifying the native handwriting.
The classification of different age groups, such as adult and child, based on handwriting is very important due to its various applications in many different fields. In forensics, handwriting classification helps investigators focus on a certain category of writers. This paper aimed to propose a machine-learning (ML)-based approach for automatically classifying people as adults or children based on their handwritten data. This study utilized two types of handwritten databases: handwritten text and handwritten pattern, which were collected using a pen tablet. The handwritten text database had 57 subjects (adult: 26 vs. child: 31). Each subject (adult or child) wrote the same 30 words using Japanese hiragana characters. The handwritten pattern database had 81 subjects (adult: 42 and child: 39). Each subject (adult or child) drew four different lines as zigzag lines (trace condition and predict condition), and periodic lines (trace condition and predict condition) and repeated these line tasks three times. Handwriting classification of adult and child is performed in three steps: (i) feature extraction; (ii) feature selection; and (iii) classification. We extracted 30 features from both handwritten text and handwritten pattern datasets. The most efficient features were selected using sequential forward floating selection (SFFS) method and the optimal parameters were selected. Then two ML-based approaches, namely, support vector machine (SVM) and random forest (RF) were applied to classify adult and child. Our findings showed that RF produced up to 93.5% accuracy for handwritten text and 89.8% accuracy for handwritten pattern databases. We hope that this study will provide the evidence of the possibility of classifying adult and child based on handwriting text and handwriting pattern data.
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