scholarly journals Zero-Shot Chinese Character Recognition with Stroke-Level Decomposition

2021 ◽  
Author(s):  
Jingye Chen ◽  
Bin Li ◽  
Xiangyang Xue
Keyword(s):  
Character Recognition ◽  
Chinese Character ◽  
Experimental Results ◽  
Research Interest ◽  
Chinese Characters ◽  
Specific Character ◽  
Know How ◽  
Chinese Character Recognition ◽  
Test Sets ◽  
Stroke Sequence

Chinese character recognition has attracted much research interest due to its wide applications. Although it has been studied for many years, some issues in this field have not been completely resolved yet, \textit{e.g.} the zero-shot problem. Previous character-based and radical-based methods have not fundamentally addressed the zero-shot problem since some characters or radicals in test sets may not appear in training sets under a data-hungry condition. Inspired by the fact that humans can generalize to know how to write characters unseen before if they have learned stroke orders of some characters, we propose a stroke-based method by decomposing each character into a sequence of strokes, which are the most basic units of Chinese characters. However, we observe that there is a one-to-many relationship between stroke sequences and Chinese characters. To tackle this challenge, we employ a matching-based strategy to transform the predicted stroke sequence to a specific character. We evaluate the proposed method on handwritten characters, printed artistic characters, and scene characters. The experimental results validate that the proposed method outperforms existing methods on both character zero-shot and radical zero-shot tasks. Moreover, the proposed method can be easily generalized to other languages whose characters can be decomposed into strokes.

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.

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 Character Recognition Using Bag of Words

2010 ◽  
Vol 20-23 ◽  
pp. 395-400 ◽  
Author(s):  
Jia Ping Gui ◽  
Yi Zhou ◽  
Xin Da Lin ◽  
Kai Chen ◽  
Hai Bing Guan
Keyword(s):  
Image Recognition ◽  
Character Recognition ◽  
Chinese Character ◽  
Experimental Results ◽  
Bag Of Words ◽  
Accuracy Rate ◽  
Chinese Character Recognition ◽  
Complex Background

The traditional OCR obtains unsatisfactory results in the field of image recognition when images are processed in a complex background with low quality. This paper presents a novel application of the model of Bag of Words on Chinese character recognition, and extensively evaluated its effectiveness with 12 different fonts of Chinese character datasets under varying circumstances. Our experimental results demonstrate that this approach can achieve nearly 70% at its highest accuracy rate, which shows its performance far exceeds the traditional OCR’s

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.

scholarly journals Holistic processing of Chinese characters in college students with dyslexia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ricky Van-yip Tso ◽  
Ronald Tsz-chung Chan ◽  
Yin-fei Chan ◽  
Dan Lin
Keyword(s):  
Character Recognition ◽  
Chinese Character ◽  
Spatial Information ◽  
Holistic Processing ◽  
Visual Object ◽  
Chinese Characters ◽  
Visual Object Recognition ◽  
Chinese Character Recognition ◽  
Visual Spatial ◽  
The Many

AbstractExpert face recognition has long been marked by holistic processing. Hence, due to the many visual properties shared between face perception and Chinese characters, it has been suggested that Chinese character recognition may induce stronger holistic processing in expert readers than in novices. However, there have been different viewpoints presented about Chinese character recognition, one of which suggests that expertise in this skill involved reduced holistic processing which may be modulated by writing experiences/performances. In this study we examined holistic processing in Chinese character recognition in adults with and without dyslexia, using the complete composite paradigm. Our results showed that the adults with dyslexia recognized Chinese characters with a stronger holistic processing effect than the typical controls. It seems that those with dyslexia relied overly on the visual spatial information of characters and showed deficits in attending selectively to their components when processing Chinese characters, which hindered the development of expert reading and writing skills. This effect was in contrast to previous perceptual expertise studies in which reduced holistic processing marked deficits in face/visual object recognition. This study is also the first to show that Chinese adults with dyslexia had persistent below average performances in Chinese literacy.

Sign in / Sign up

Export Citation Format

Share Document