Automatic generation of Chinese calligraphic writings with style imitation

A parametric representation of stroke shapes is derived by adopting style imitation, a shape-generation-based process, to compactly represent the shapes of single strokes for the automatic generation of Chinese calligraphic writings. An image-processing-based approach is employed to derive the distance between the two strokes. The concept of stroke context is introduced to determine the shape of a stroke to be produced and the distance between two strokes is defined as the shortest distance between two points. An important difference between personal handwriting and a script generated from a font system is that a human writer writes a certain stroke or character differently each time, while a font system generates the same output. The shape-based criterion used in this study makes direct use of areas, which is more reliable than using shape contours because stroke contour details vary greatly.published_or_final_versio

Special Radical Detection by Statistical Classification for On-line Handwritten Chinese Character Recognition

International audienceThe hierarchical nature of Chinese characters has inspired radical-based recognition, but radical segmentation from characters remains a challenge. We previously proposed a radical-based approach for on-line handwritten Chinese character recognition, which incorporates character structure knowledge into integrated radical segmentation and recognition, and performs well on characters of left-right and up-down structures (non-special structures). In this paper, we propose a statistical-classification-based method for detecting special radicals from special-structure characters. We design 19 binary classifiers for classifying candidate radicals (groups of strokes) hypothesized from the input character. Characters with special radicals detected are recognized using special-structure models, while those without special radicals are recognized using the models for non-special structures. We applied the recognition framework to 6,763 character classes, and achieved promising recognition performance in experiments

Rotation-invariant features for multi-oriented text detection in natural images.

Texts in natural scenes carry rich semantic information, which can be used to assist a wide range of applications, such as object recognition, image/video retrieval, mapping/navigation, and human computer interaction. However, most existing systems are designed to detect and recognize horizontal (or near-horizontal) texts. Due to the increasing popularity of mobile-computing devices and applications, detecting texts of varying orientations from natural images under less controlled conditions has become an important but challenging task. In this paper, we propose a new algorithm to detect texts of varying orientations. Our algorithm is based on a two-level classification scheme and two sets of features specially designed for capturing the intrinsic characteristics of texts. To better evaluate the proposed method and compare it with the competing algorithms, we generate a comprehensive dataset with various types of texts in diverse real-world scenes. We also propose a new evaluation protocol, which is more suitable for benchmarking algorithms for detecting texts in varying orientations. Experiments on benchmark datasets demonstrate that our system compares favorably with the state-of-the-art algorithms when handling horizontal texts and achieves significantly enhanced performance on variant texts in complex natural scenes

Investigating Semantic Alignment in Character Learning of Chinese as a Foreign Language: The Use and Effect of the Imagery Based Encoding Strategy

For learners of Chinese as a foreign language (CFL), character learning is frustrating. This research postulated that this difficulty may mainly come from a lack of semantic understanding of character-denoted meanings. Language theories support that when a learner’s semantic meaning increases, the orthographic structures that represent the underlying meanings also improve. This study aimed to reveal CFL learners’ cognitive abilities and processes in visual-semantic learning of Chinese characters. Particularly, this study investigated the process by which English-speaking adolescent CFL learners, at the beginning to intermediate level, made mental images of character-denoted meanings to visually encode and retrieve character forms. Quantitative and qualitative data were gathered from image making questionnaires, writing, and reading tests, after learning characters in three commonly-used teaching methods (i.e., English, pictorial, and verbal). The data were analyzed based on a triangulation of the literature from Neuro-Semantic Language Learning Theory, scientific findings in cognitive psychology, and neuroscience. The study found that participants’ semantic abilities to understand character-denoted meanings emerged, but were still restricted in familiar orthographic forms. The use of the imagery strategy as a semantic ability predicted better performances, most evidently in writing; however, the ability in using the imagery strategy to learn characters was still underdeveloped, and needed to be supported with sufficient contextual information. Implications and further research in visual-semantic learning and teaching characters were suggested

Writing quality predicts Chinese learning

To examine the importance of manual character writing to reading in a new writing system, 48 adult Chinese-as-a-foreign-language students were taught characters in either a character writing-to-read or an alphabet typing-to-read condition, and engaged in corresponding handwriting or typing training for five consecutive days. Prior knowledge of orthography and phonology was assessed before training. At the end of each training day, improved orthographic quality was assessed via increased skill in producing Chinese characters at both the component and global levels. In addition, pretests and posttests were administered at each training day, and the proportional changes were used as the measure of learning gains. Outcomes replicated earlier findings of improved phonological knowledge following pinyin-typing practice and improved semantic knowledge following handwriting practice. Improvement in handwriting quality played a significant role in predicting reading gains after controlling for prior knowledge

The reading of handwriting: An evaluation of Chinese written by CFL learners.

This paper describes two experiments that first explore the potential role of Chinese character writing in character visual recognition, and then examine different evaluative responses towards the quality of pinyin and character handwriting. Taken together, the results suggest that drawing Chinese characters privileges them in memory in a way that facilitates their subsequent visual recognition. This is true even when the congruency of the recognition response and other potential confounds are controlled for. In terms of the writing quality, the reader’s empathy effect can be found for handwritten characters but not pinyin, since the handwritten characters tended to be rated more highly than pinyin. The experience of an evaluator also has an impact on the evaluation of writing quality. The pedagogical implications for Chinese as a foreign language (CFL) are highlighted at the end of the paper, in particular those relating to curriculum design and teacher training

Character Recognition

Character recognition is one of the pattern recognition technologies that are most widely used in practical applications. This book presents recent advances that are relevant to character recognition, from technical topics such as image processing, feature extraction or classification, to new applications including human-computer interfaces. The goal of this book is to provide a reference source for academic research and for professionals working in the character recognition field

The reading of handwriting: an evaluation of Chinese written by CFL learners

This paper describes two experiments that first explore the potential role of Chinese character writing in character visual recognition, and then examine different evaluative responses towards the quality of pinyin and character handwriting. Taken together, the results suggest that drawing Chinese characters privileges them in memory in a way that facilitates their subsequent visual recognition. This is true even when the congruency of the recognition response and other potential confounds are controlled for. In terms of the writing quality, the reader’s empathy effect can be found for handwritten characters but not pinyin, since the handwritten characters tended to be rated more highly than pinyin. The experience of an evaluator also has an impact on the evaluation of writing quality. The pedagogical implications for Chinese as a foreign language (CFL) are highlighted at the end of the paper, in particular those relating to curriculum design and teacher training

Representation, Recognition and Collaboration with Digital Ink

Pen input for computing devices is now widespread, providing a promising interaction mechanism for many purposes. Nevertheless, the diverse nature of digital ink and varied application domains still present many challenges. First, the sampling rate and resolution of pen-based devices keep improving, making input data more costly to process and store. At the same time, existing applications typically record digital ink either in proprietary formats, which are restricted to single platforms and consequently lack portability, or simply as images, which lose important information. Moreover, in certain domains such as mathematics, current systems are now achieving good recognition rates on individual symbols, in general recognition of complete expressions remains a problem due to the absence of an effective method that can reliably identify the spatial relationships among symbols. Last, but not least, existing digital ink collaboration tools are platform-dependent and typically allow only one input method to be used at a time. Together with the absence of recognition, this has placed significant limitations on what can be done. In this thesis, we investigate these issues and make contributions to each. We first present an algorithm that can accurately approximate a digital ink curve by selecting a certain subset of points from the original trace. This allows a compact representation of digital ink for efficient processing and storage. We then describe an algorithm that can automatically identify certain important features in handwritten symbols. Identifying the features can help us solve a number of problems such as improving two-dimensional mathematical recognition. Last, we present a framework for multi-user online collaboration in a pen-based and graphical environment. This framework is portable across multiple platforms and allows multimodal interactions in collaborative sessions. To demonstrate our ideas, we present InkChat, a whiteboard application, which can be used to conduct collaborative sessions on a shared canvas. It allows participants to use voice and digital ink independently and simultaneously, which has been found useful in remote collaboration