Research on Recognition Effect of DSCN Network Structure in Hand-Drawn Sketch

With the rapid development of image recognition technology, freehand sketch recognition has attracted more and more attention. How to achieve good recognition effect in the absence of color and texture information is the key to the development of freehand sketch recognition. Traditional nonlearning classical models are highly dependent on manual selection features. To solve this problem, a neural network sketch recognition method based on DSCN structure is proposed in this paper. Firstly, the stroke sequence of the sketch is drawn; then, the feature is extracted according to the stroke sequence combined with neural network, and the extracted image features are used as the input of the model to construct the time relationship between different image features. Through the control experiment on TU-Berlin dataset, the results show that, compared with the traditional nonlearning methods, HOG-SVM, SIFT-Fisher Vector, MKL-SVM, and FV-SP, the recognition accuracy of DSCN network is improved by 15.8%, 10.3%, 6.0%, and 2.9%, respectively. Compared with the classical deep learning model, Alex-Net, the recognition accuracy is improved by 5.6%. The above results show that the DSCN network proposed in this paper has strong ability of feature extraction and nonlinear expression and can effectively improve the recognition accuracy of hand-painted sketches after introducing the stroke order.

Zero-Shot Chinese Character Recognition with Stroke-Level Decomposition

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.

Parietal Dysgraphia: Characterization of Abnormal Writing Stroke Sequences, Character Formation and Character Recall

Objective:To characterize various dysgraphic symptoms in parietal agraphia.Method:We examined the writing impairments of four dysgraphia patients from parietal lobe lesions using a special writing test with 100 character kanji (Japanese morphograms) and their kana (Japanese phonetic writing) transcriptions, and related the test performance to a lesion site.Results:Patients 1 and 2 had postcentral gyrus lesions and showed character distortion and tactile agnosia, with patient 1 also having limb apraxia. Patients 3 and 4 had superior parietal lobule lesions and features characteristic of apraxic agraphia (grapheme deformity and a writing stroke sequence disorder) and character imagery deficits (impaired character recall). Agraphia with impaired character recall and abnormal grapheme formation were more pronounced in patient 4, in whom the lesion extended to the inferior parietal, superior occipital and precuneus gyri.Conclusion:The present findings and a review of the literature suggest that: (i) a postcentral gyrus lesion can yield graphemic distortion (somesthetic dysgraphia), (ii) abnormal grapheme formation and impaired character recall are associated with lesions surrounding the intraparietal sulcus, the symptom being more severe with the involvement of the inferior parietal, superior occipital and precuneus gyri, (iii) disordered writing stroke sequences are caused by a damaged anterior intraparietal area.