4,215 research outputs found
Text-independent writer identification using convolutional neural network
The text-independent approach to writer identification does not require the writer to write some predetermined text. Previous research on text-independent writer identification has been based on identifying writer-specific features designed by experts. However, in the last decade, deep learning methods have been successfully applied to learn features from data automatically. We propose here an end-to-end deep-learning method for text-independent writer identification that does not require prior identification of features. A Convolutional Neural Network (CNN) is trained initially to extract local features, which represent characteristics of individual handwriting in the whole character images and their sub-regions. Randomly sampled tuples of images from the training set are used to train the CNN and aggregate the extracted local features of images from the tuples to form global features. For every training epoch, the process of randomly sampling tuples is repeated, which is equivalent to a large number of training patterns being prepared for training the CNN for text-independent writer identification. We conducted experiments on the JEITA-HP database of offline handwritten Japanese character patterns. With 200 characters, our method achieved an accuracy of 99.97% to classify 100 writers. Even when using 50 characters for 100 writers or 100 characters for 400 writers, our method achieved accuracy levels of 92.80% or 93.82%, respectively. We conducted further experiments on the Firemaker and IAM databases of offline handwritten English text. Using only one page per writer to train, our method achieved over 91.81% accuracy to classify 900 writers. Overall, we achieved a better performance than the previously published best result based on handcrafted features and clustering algorithms, which demonstrates the effectiveness of our method for handwritten English text also
A Sketch-Based Educational System for Learning Chinese Handwriting
Learning Chinese as a Second Language (CSL) is a difficult task for students in English-speaking countries due to the large symbol set and complicated writing techniques. Traditional classroom methods of teaching Chinese handwriting have major drawbacks due to human experts’ bias and the lack of assessment on writing techniques. In this work, we propose a sketch-based educational system to help CSL students learn Chinese handwriting faster and better in a novel way. Our system allows students to draw freehand symbols to answer questions, and uses sketch recognition and AI techniques to recognize, assess, and provide feedback in real time. Results have shown that the system reaches a recognition accuracy of 86% on novice learners’ inputs, higher than 95% detection rate for mistakes in writing techniques, and 80.3% F-measure on the classification between expert and novice handwriting inputs
Corrective Feedback Timing in Kanji Writing Instruction Apps
The focus of this research paper is to determine the correct time to provide corrective feedback to people who are learning how to write Japanese kanji. To do this, we developed a system that is able to recognize Japanese kanji that is handwritten onto an iPad screen and check for errors such as wrong stroke order. Previous research has achieved success in developing similar systems, but this project is unique because the research question involves the timing of corrective feedback. In particular, we are looking at whether immediate or delayed corrective feedback results in better learning
Recognition of handwritten Arabic characters
The subject of handwritten character recognition has been receiving considerable attention in recent years due to the increased dependence on computers. Several methods for recognizing Latin, Chinese as well as Kanji characters have been proposed. However, work on recognition of Arabic characters has been relatively sparse. Techniques developed for recognizing characters in other languages can not be used for Arabic since the nature of Arabic characters is different. The shape of a character is a function of its location within a word where each character can have two to four different forms. Most of the techniques proposed to date for recognizing Arabic characters have relied on structural and topographic approaches.
This thesis introduces a decision-theoretic approach to solve the problem. The proposed method involves, as a first step, digitization of the segmented character. The secondary part of the character (dots and zigzags) are then isolated and identified separately thereby reducing the recognition issue to a 20 class problem or less for each of the character forms. The moments of the horizontal and vertical projections of the remaining primary characters are calculated and normalized with respect to the zero order moment. Simple measures of shape are obtained from the normalized moments and incorporated into a feature vector. Classification is accomplished using quadratic discriminant functions. The approach was evaluated using isolated, handwritten characters from a data base established for this purpose. The classification rates varied from 97.5% to 100% depending on the form of the characters. These results indicate that the technique offers significantly better classification rates in comparison with existing methods
Advances in Character Recognition
This book presents advances in character recognition, and it consists of 12 chapters that cover wide range of topics on different aspects of character recognition. Hopefully, this book will serve as a reference source for academic research, for professionals working in the character recognition field and for all interested in the subject
シンソウニューラルネットワークニヨルテガキテキストニンシキ
博士(工学)東京農工大
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