The effectiveness of guided inductive instruction and deductive instruction on semantic radical development in Chinese character processing

2018 ◽  
Vol 24 (4) ◽  
pp. 496-518 ◽  
Author(s):  
Chun Lai ◽  
Xuedan Qi ◽  
Chan Lü ◽  
Boning Lyu
Keyword(s):  
Character Recognition ◽  
Chinese Character ◽  
Future Research ◽  
Chinese Characters ◽  
Quasi Experimental ◽  
Semantic Radical ◽  
Guided Inductive ◽  
Learning And Instruction ◽  
Radical Form ◽  
Deductive Instruction

This study compared the effectiveness of deductive instruction and guided inductive instruction for developing semantic radical knowledge of Chinese characters. The evaluation was conducted through a quasi-experimental 3-week intervention involving 46 intermediate learners of Chinese as a foreign language (CFL). The results indicated that guided inductive instruction generated significantly greater gains in learners’ use of radical information for radical form-meaning mapping and for Chinese character recognition and inferencing. This study further found that the effectiveness of inductive instruction in strengthening radical form-meaning mapping varied for semantic radicals of different complexity levels. These findings suggest that instructors should apply guided induction in teaching semantic radicals, but also be flexible in varying instruction in response to the complexity of semantic radicals. The findings suggest that the inductive-deductive nature of instruction and the complexity of semantic radicals are important variables to consider in future research on the learning and instruction of Chinese characters.

A research on an intelligent recognition tool for bronze inscriptions of the Shang and Zhou dynasties

2020 ◽  
Vol 4 (4) ◽  
pp. 271-279
Author(s):  
Rui Guo
Keyword(s):  
Computer Technology ◽  
Character Recognition ◽  
Chinese Character ◽  
Research Process ◽  
Chinese Characters ◽  
Chinese Character Recognition ◽  
The World ◽  
Bronze Inscriptions ◽  
Intelligent Recognition ◽  
Functional Components

The intelligent recognition tool for bronze inscriptions of the Shang and Zhou dynasties—the “Shang Zhou Bronze Inscriptions Intelligent Mirror”—was successfully invented in Shanghai. This mirror, based on the computer technology of artificial intelligence (AI) image recognition and image retrieval, succeeds in automagical recognition of bronze inscriptions, both single letters and full texts. This research leads the trend of the AI recognition of Ancient Chinese characters and accumulates valuable experience for the development of inter-disciplinary research on Chinese character recognition. This essay emphasizes the importance of the bronze inscriptions of the Shang and Zhou dynasty database in the AI recognition of bronze inscriptions, introduces the functional components of this tool, and shares the whole research process in order to offer experience for the related research on AI recognition of other types of Ancient Chinese characters as well as ideographs in the world scope. “Shang Zhou Bronze Inscriptions Intelligent Mirror” as a tool for bronze inscription recognition also has room for improvement and support, and guidance from experts in similar areas is greatly welcomed.

On-Line Chinese Character Recognition Via a Representation of Spatial Relationships Between Strokes

1997 ◽  
Vol 11 (03) ◽  
pp. 329-357 ◽  
Author(s):  
Ju-Wei Chen ◽  
Suh-Yin Lee
Keyword(s):  
Character Recognition ◽  
Chinese Character ◽  
Spatial Relationship ◽  
Spatial Relations ◽  
Chinese Characters ◽  
Spatial Relationships ◽  
Matching Problem ◽  
Structural Rules ◽  
Chinese Character Recognition ◽  
On Line

Chinese characters are constructed by basic strokes based on structural rules. In handwritten characters, the shapes of the strokes may vary to some extent, but the spatial relations and geometric configurations of the strokes are usually maintained. Therefore these spatial relations and configurations could be regarded as invariant features and could be used in the recognition of handwritten Chinese characters. In this paper, we investigate the structural knowledge in Chinese characters and propose the stroke spatial relationship representation (SSRR) to describe Chinese characters. An On-Line Chinese Character Recognition (OLCCR) method using the SSRR is also presented. With SSRR, each character is processed and is represented by an attribute graph. The process of character recognition is thereby transformed into a graph matching problem. After careful analysis, the basic spatial relationship between strokes can be characterized into five classes. A bitwise representation is adopted in the design of the data structure to reduce storage requirements and to speed up character matching. The strategy of hierarchical search in the preclassification improves the recognition speed. Basically, the attribute graph model is a generalized character representation that provides a useful and convenient representation for newly added characters in an OLCCR system with automatic learning capability. The significance of the structural approach of character recognition using spatial relationships is analyzed and is proved by experiments. Realistic testing is provided to show the effectiveness of the proposed method.

scholarly journals Double-Sided Occluded Chinese Character Recognition Accuracy and Response Time for Design and Nondesign Educational Background

SAGE Open ◽  
2018 ◽  
Vol 8 (4) ◽  
pp. 215824401881006
Author(s):  
Ching-Chih Liao
Keyword(s):  
Response Time ◽  
Character Recognition ◽  
Visual Recognition ◽  
Chinese Character ◽  
Recognition Accuracy ◽  
Educational Status ◽  
Educational Background ◽  
Chinese Characters ◽  
Chinese Character Recognition ◽  
Educational Groups

This article investigates the influence of the position of occlusion, structural composition, and design educational status on Chinese character recognition accuracy and response time. Tsao and Liao conducted an experiment using 18 of the 4,000 most commonly used Chinese characters and suggested that the primary and secondary recognition features of a “single-sided” occluded Chinese character are the key radical (or initial strokes) and the key component (i.e., combination of strokes), respectively. The study concluded that right-side occluded characters require a shorter response time and yield more accurate recognition and that educational background does not significantly affect recognition accuracy and response time. The present study considered the same 18 Chinese characters and extended the work of Tsao and Liao by exploring accuracy rate and response time in design and nondesign educational groups for the recognition of “double-sided” occluded Chinese characters. The experimental results indicated that right-side occlusion (including both bottom-right and top-right occlusion) requires a shorter response time and yields more accurate recognition than left-side occlusion. These results agree with those of Tsao and Liao, who found that the key radical of a Chinese character is its key visual recognition feature. Even double-sided occlusion of Chinese characters does not affect the recognition outcome if the position of occlusion does not blur the key radical. Moreover, the participants majoring in design recognized the occluded Chinese characters more slowly than those with no educational background in design.

A TMS examination of semantic radical combinability effects in Chinese character recognition

2006 ◽  
Vol 1078 (1) ◽  
pp. 159-167 ◽  
Author(s):  
Janet Hui-wen Hsiao ◽  
Richard Shillcock ◽  
Michal Lavidor
Keyword(s):  
Character Recognition ◽  
Chinese Character ◽  
Chinese Character Recognition ◽  
Semantic Radical

HANDPRINTED CHINESE CHARACTER RECOGNITION USING THE PROBABILITY DISTRIBUTION FEATURE

1994 ◽  
Vol 08 (05) ◽  
pp. 1241-1258 ◽  
Author(s):  
TZE FEN LI ◽  
SHIAW-SHIAN YU
Keyword(s):  
Probability Distribution ◽  
Character Recognition ◽  
Chinese Character ◽  
Recognition Rate ◽  
Bayes Rule ◽  
Chinese Characters ◽  
Two Dimensional ◽  
Shape Variation ◽  
Recognition Method ◽  
Chinese Character Recognition

A simplified Bayes rule is used to classify 5401 categories of handwritten Chinese characters. The main feature for the Bayes rule deals with the probability distribution of black pixels of a thinned character. Our idea is that each Chinese character indicated by the black pixels represents a probability distribution in a two-dimensional plane. Therefore, an unknown pattern is classified into one of 5401 different distributions by the Bayes rule. Since the handwritten character has an irregular shape variation, the whole character is normalized and then thinned. Finally, a transformation is used to spread the black pixels uniformly over the whole square plane, but it still keeps the relative positions of the original black pixels. The main feature gives an 88.65% recognition rate. In order to raise the recognition rate, 4 more subsidiary features are elaborately selected such that they are not affected much by the irregularly shaped variation. The 4 features raise the recognition rate to 93.43%. A 99.30% recognition rate is achieved if the top 10 categories of HCC are selected by our recognition method and 99.61% if the top 20 are selected.

A Preclassification Method for Handwritten Chinese Character Recognition Via Fuzzy Rules and Seart Neural Net

1998 ◽  
Vol 12 (06) ◽  
pp. 743-761 ◽  
Author(s):  
Hahn-Ming Lee ◽  
Chin-Chou Lin ◽  
Jyh-Ming Chen
Keyword(s):  
Character Recognition ◽  
Chinese Character ◽  
Fuzzy Rules ◽  
Stage I ◽  
Chinese Characters ◽  
Neural Net ◽  
Chinese Character Recognition ◽  
Handwritten Chinese Character Recognition ◽  
Two Stages ◽  
Testing Set

In this paper, a method of character preclassification for handwritten Chinese character recognition is proposed. Since the number of Chinese characters is very large (at least 5401s for daily use), we employ two stages to reduce the candidates of an input character. In stage I, we extract the first set of primitive features from handwritten Chinese characters and use fuzzy rules to create four preclassification groups. The purpose in stage I is to reduce the candidates roughly. In stage II, we extract the second set of primitive features from handwritten Chinese characters and then use the Supervised Extended ART (SEART) as the classifier to generate preclassification classes for each preclassification group created in stage I. Since the number of characters in each preclassification class is smaller than that in the whole character set, the problem becomes simpler. In order to evaluate the proposed preclassification system, we use 605 Chinese character categories in the textbooks of elementary school as our training and testing data. The database used is HCCRBASE (provided by CCL, ITRI, Taiwan). In samples 1–100, we select the even samples as the training set, and the odd samples as the testing set. The characters of the testing set can be distributed into correct preclassification classes at a rate of 98.11%.

RESEARCH ON CHINESE OCR IN TAIWAN

1991 ◽  
Vol 05 (01n02) ◽  
pp. 139-164 ◽  
Author(s):  
FANG-HSUAN CHENG ◽  
WEN-HSING HSU
Keyword(s):  
Feature Extraction ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Chinese Character ◽  
Chinese Characters ◽  
Image Preprocessing ◽  
Large Sets ◽  
Basic Line ◽  
Optical Character ◽  
Taiwan Chinese

This paper describes typical research on Chinese optical character recognition in Taiwan. Chinese characters can be represented by a set of basic line segments called strokes. Several approaches to the recognition of handwritten Chinese characters by stroke analysis are described here. A typical optical character recognition (OCR) system consists of four main parts: image preprocessing, feature extraction, radical extraction and matching. Image preprocessing is used to provide the suitable format for data processing. Feature extraction is used to extract stable features from the Chinese character. Radical extraction is used to decompose the Chinese character into radicals. Finally, matching is used to recognize the Chinese character. The reasons for using strokes as the features for Chinese character recognition are the following. First, all Chinese characters can be represented by a combination of strokes. Second, the algorithms developed under the concept of strokes do not have to be modified when the number of characters increases. Therefore, the algorithms described in this paper are suitable for recognizing large sets of Chinese characters.

Phonological, not semantic, activation dominates Chinese character recognition: Evidence from a visual world eye-tracking study

2019 ◽  
Vol 73 (4) ◽  
pp. 617-628
Author(s):  
Xiuhong Tong ◽  
Wei Shen ◽  
Zhao Li ◽  
Mengdi Xu ◽  
Liping Pan ◽  
...  
Keyword(s):  
Eye Tracking ◽  
Character Recognition ◽  
Semantic Information ◽  
Chinese Character ◽  
Semantic Activation ◽  
Visual World ◽  
Visual World Paradigm ◽  
Chinese Character Recognition ◽  
Eye Fixations ◽  
Semantic Radical

Combining eye-tracking technique with a revised visual world paradigm, this study examined how positional, phonological, and semantic information of radicals are activated in visual Chinese character recognition. Participants’ eye movements were tracked when they looked at four types of invented logographic characters including a semantic radical in the legal (e.g., [Formula: see text]) and illegal positions ([Formula: see text]), a phonetic radical in the legal (e.g., [Formula: see text]) and illegal positions (e.g., [Formula: see text]). These logographic characters were presented simultaneously with either a sound-cued (e.g., /qiao2/) or meaning-cued (e.g., a picture of a bridge) condition. Participants appeared to allocate more visual attention towards radicals in legal, rather than illegal, positions. In addition, more eye fixations occurred on phonetic, rather than on semantic, radicals across both sound- and meaning-cued conditions, indicating participants’ strong preference for phonetic over semantic radicals in visual character processing. These results underscore the universal phonology principle in processing non-alphabetic Chinese logographic characters.

STRUCTURAL ATTRIBUTE FEATURE CODE REPRESENTATION AND RECOGNITION OF MULTIFONT PRINTED CHINESE CHARACTERS

2001 ◽  
Vol 15 (02) ◽  
pp. 287-309
Author(s):  
ZHEN YONG LIN ◽  
PING LIU
Keyword(s):  
Character Recognition ◽  
Chinese Character ◽  
Chinese Characters ◽  
Structural Representation ◽  
Different Types ◽  
Fuzzy Matching ◽  
Structural Attribute ◽  
High Recognition Accuracy ◽  
Global And Local ◽  
Input Character

In this paper, a new structural representation and fuzzy matching scheme are proposed for multifont printed Chinese character recognition. A Chinese character is decomposed into eight stroke types. A complete structural attribute feature codes among different types of strokes are defined and extracted, which consist of weak and strong primary codes and secondary codes. Weak and strong primary feature codes depict the global and local spatial relationships among different types of strokes respectively, and they are used for a detailed match. A fuzzy matching scheme is used for detailed match between an input character and candidate characters. An experiment on 3755 Chinese characters used daily in multifonts and multisizes shows that our method is robust and can achieve high recognition accuracy.

Preliminary study of the effect of Incremental Rehearsal with a morphological component for teaching Chinese character recognition

2016 ◽  
Vol 37 (6) ◽  
pp. 627-643
Author(s):  
Elena Kwong ◽  
Matthew K. Burns
Keyword(s):  
Character Recognition ◽  
Chinese Character ◽  
Single Case ◽  
Kindergarten Students ◽  
Chinese Characters ◽  
Incremental Rehearsal ◽  
Chinese Character Recognition ◽  
Morphological Component ◽  
Teaching Chinese

The current study examined the effectiveness of Incremental Rehearsal (IR) for teaching Chinese character recognition using a single-case experimental design. In addition, a morphological component was added to standard IR procedures (IRM) to take into account the role of morphological awareness in Chinese reading. Three kindergarten students in Hong Kong who were learning Cantonese-Chinese were taught Chinese characters with IR and IRM over six weeks using two ABAB designs. The study found that both IR and IRM effectively increased retention and maintenance of Chinese characters.

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