A Chinese character is usually composed of several graphical components, including a radical and several other different parts. Radicals are the graphical components used to index Chinese characters in a dictionary. Characters with the same component may share similar semantic or phonetic properties [1,2,3]. The analysis and processing of Chinese characters have enjoyed a long history [4], and recently have garnered much attention with the development of artificial intelligence techniques, such as machine learning [5] and deep learning [6] as well as several related applications (e.g., recognition of Chinese characters [7] and natural language understanding of Chinese documents [8]).
1.1 Chinese character decomposition
To online process Chinese documents stored as binary images, it would be helpful if the Chinese characters could automatically be efficiently decomposed in advance. However, automatically decomposing a Chinese character (represented as a binary image) into different components is difficult and has rarely been investigated in the literature.
Based on our explorations of works relating to Chinese characters, most prior research has aimed at extracting Chinese character strokes via automatic character decomposition [9,10,11,12,13]. Strokes are the fundamental building blocks of Chinese characters and are needed to write Chinese characters in regular script [14]. To decompose a Chinese character into a set of strokes, a mathematical morphology-based method was proposed in [11]. A model of stroke extraction for Chinese characters was also proposed in [9] to extract primary strokes. In [13], a Chinese character is decomposed into isolated stroke structures based on the connections. Then, shape contexts of stroke structures are extracted, and the matched counterparts in a standard database are found through shape matching. Moreover [10], was proposed building a font skeleton manifold so that the most similar character could be always found as a template by traversing the locations in the manifold learned for the application of stroke extraction for Chinese characters. Meanwhile [12], was proposed starting with automatically decomposing a Chinese character image into strokes, and then separately resizing those strokes under the guidance of structure information to achieve structure-aware image resizing for Chinese characters.
These works on decomposing Chinese characters into strokes were mainly designed for applications in character recognition, writing style analysis, and new font synthesis [15]. Such methods usually rely on a standard database consisting of standard Chinese character templates or standard strokes, used for shape/template matching.
On the other hand, to better capture semantics of natural language words, low-dimensional distributed word representations, also known as word embeddings, were introduced recently [16, 17]. For example, a method, called cw2vec, for learning Chinese word embeddings with stroke n-gram information was proposed in [16]. More specifically, a minimalist approach was designed to exploit the stroke n-grams, which can capture semantic and morphological level information of Chinese words. Moreover, to improve word embeddings, a hybrid learning method, integrating compositional, and predictive models for word embeddings was presented in [17]. In general, word embedding techniques have been shown to be applicable in the tasks of word similarity, word analogy, text classification, and named entity recognition. However, the main goal of word embeddings is usually to better capture semantics of natural language words, which is essentially different from that of the proposed method described in the next subsection.
1.2 Main objective of this paper
In significant contrast to the goals and approaches of the above-mentioned works, the goal of this paper is to develop an automatic Chinese character decomposition framework to decompose a Chinese character into several graphical components without considering the strokes of the character or any semantic or phonetic properties of the components, as in the examples illustrated in Fig. 1. For example, the Chinese character ‘好’ will be automatically decomposed into the components ‘女’ and ‘子’ without considering the semantic or phonetic properties of either of the two components. That is, we intend to automatically achieve graphical decomposition of Chinese characters represented as binary images without the need for any prior knowledge of semantic and phonetic properties of Chinese character components or character strokes.
To achieve our goal, we propose applying non-negative matrix factorization (NMF) technique to decompose a Chinese character into different components. In general, NMF (or non-negative matrix approximation) [18,19,20,21] intends to factorize a matrix into two matrices, with all three matrices having no negative elements. The property of non-negativity makes the resulting matrices easier to inspect; therefore, NMF has been successfully applied to several source decomposition applications in digital audio signals [22,23,24], digital visual signals [25,26,27,28,29], and document clustering [30].
However, from the viewpoint of image signal decomposition using NMF [15, 26,27,28,29], the standard NMF technique [18, 19] is only suitable for processing general digital grayscale or color images, not for binary images. In general, Chinese characters are usually presented in binary tones, black and white. Therefore, this paper presents a novel NMF framework to factorize a Chinese character represented as a binary image (or matrix). Furthermore, we evaluate the performance of the proposed NMF technique by applying it to the application of visual secret sharing (a visual cryptographic technique [31,32,33,34,35] in information security [36,37,38]) that securely shares secret messages encoded as binary images of partial Chinese characters.
1.3 Main contributions of this paper
The major innovations and contributions of this paper are three-fold: (a) to the best of our knowledge, we are among the first to propose an automatic Chinese character decomposition framework to break a character into graphical components without a need for prior knowledge of semantic or phonetic properties of Chinese character components or character strokes; (b) to achieve this goal, we propose a novel NMF framework to factorize a binary image into two matrices while forcing all of the elements of the two matrices as close to 0 or 1 as possible; and (c) we successfully apply our NMF-based Chinese character decomposition technique to visual secret sharing for the secure transmission of secret messages encoded by binary images of Chinese characters.
The rest of this paper is organized as follows. In Section 2, the standard NMF technique [18, 19] is briefly reviewed, followed by a presentation of the proposed novel NMF framework in Sections 3 and 4, we present the evaluation results by applying the proposed NMF-based Chinese character decomposition method to the application of visual secret sharing. Finally, Section 7 concludes this paper.