# Image-Based Radical Identification in Chinese Characters
## 4 Discussion
### Related and Future Works  
The radical feature detector implemented by [20,23,24] is comparatively much more complex. For instance, [PERSON]. [23,24] considered the hanzi as linearized hierarchical radical structures to solve the character recognition in handwriting and natural scenes, respectively. An unknown character is hence identifiable by its total decomposition into a string of sub-lexical units followed by a search in a dictionary. [PERSON]. [20] adopted a similar concept to analyze intrinsic emotions in texts according to shared sub-lexical features. Although the hanzi is not explicitly identified in this case, it remains a hanzi recognition problem because a closed set of sub-lexical elements narrows down the candidate characters. Therefore, despite the same terminology, the radical information they extracted is not one of the 214 indexing radicals from dictionaries, but rather the 485 sub-lexical units the Chinese characters can be decomposed into according to the GB18030 standard.  
On the other hand, [PERSON]. [17; 18] classified digitized contents according to the indexing radical. For instance, Ref. [18] proposed to tag disease-related texts from non-related ones by searching for the indexing radical that groups the hanzi expressing \"disease\" or \"illness\". Albeit exploring the indexing radical information and not the sub-lexical units, both works rely on digitized input data, i.e., the hanzi are expected to be Unicode, thus requiring an extra step of digitizing a given text if it is printed material. Nowadays, OCR technology is a reliable technology to recover the contents of printed material, especially for western languages, but it still presents limitations for Chinese texts: most OCR tools are direction-sensitive, but the biggest challenge is that the Chinese characters are very similar. Moreover, OCR usually supports only the most common press font and begins to fail in detecting the contents even when the font style is just a little flourished, such as brush calligraphy [25; 26].  
In this sense, although the identifier we proposed is deficient to say which is the input hanzi, it does not require the input data to be digitized Unicode, making it useful when a digital version of the text is unavailable, and OCR fails. This feature is handy considering that modern smartphones all have built-in cameras, and it is often easier to take a picture of a text than to type it in. Since our hanzi extractor is based on the in-line spacing options, however, the routine requires input images with well-aligned vertical and horizontal lines between adjacent hanzi. This routine should be improved if one wants to use any image from natural scenes for varying rotation, scaling, or even randomly distributed characters. This task remains an input extraction problem and does not relate to the indexing radical classification performance.  
Afterward, the proposed radical identifier has potential applications in natural language processing and multimedia Chinese learning tools when character recognition is not essential. For instance, radical knowledge contributes to text classification, information extraction, and data storage algorithms. Moreover, since it is fundamental in understanding and meaning inference, the proposed radical identifier can be implemented into a game or an application to aid hanzi learning, e.g., by highlighting the indexing radical of the hanzi from a picture. The image input is relevant in this case because a beginner learner might not have the means to input a hanzi as a raw text element. Finally, the proposed classifier can be extended to other Asian languages with script systems derived from Han characters, such as Japanese and Korean. Furthermore, one can extend the concept to ancient variants of Chinese characters, such as Tangut script or of other ancient Dynasties as well as the individual systems adopted by the Six Kingdoms before the character unification by [PERSON] [33; 34].