Handwritten Yi Character Recognition with Density-Based Clustering Algorithm and Convolutional Neural Network

Jia, Xiaodong; Gong, Wendong; Yuan, Jie

doi:10.1109/cse-euc.2017.67

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…Furthermore, it is worth noting that the ODConv exhibits a lower computational cost than standard convolution, as deduced from the computation of dhwk 3 C in C out according to Eq. (2).…”

Section: Yi Character Detection Based On Omni-dimensional Dynamic Con...mentioning

confidence: 99%

“…Consequently, there is an urgent need for the digital preservation of Yi literature, which has the potential to unearth the profound cultural treasures and spiritual essence embedded in Yi character's writings. Accurate recognition of Yi characters serves as a crucial foundation for advancing the digitization of Yi literature, the quality of which directly affects subsequent identification and analysis efforts [1,2].…”

Section: Introductionmentioning

confidence: 99%

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A Method for Detecting and Recognizing Yi Character Based on Deep Learning

Sun,

Ding,

Sun

et al. 2024

CMC

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Aiming at the challenges associated with the absence of a labeled dataset for Yi characters and the complexity of Yi character detection and recognition, we present a deep learning-based approach for Yi character detection and recognition. In the detection stage, an improved Differentiable Binarization Network (DBNet) framework is introduced to detect Yi characters, in which the Omni-dimensional Dynamic Convolution (ODConv) is combined with the ResNet-18 feature extraction module to obtain multi-dimensional complementary features, thereby improving the accuracy of Yi character detection. Then, the feature pyramid network fusion module is used to further extract Yi character image features, improving target recognition at different scales. Further, the previously generated feature map is passed through a head network to produce two maps: a probability map and an adaptive threshold map of the same size as the original map. These maps are then subjected to a differentiable binarization process, resulting in an approximate binarization map. This map helps to identify the boundaries of the text boxes. Finally, the text detection box is generated after the post-processing stage. In the recognition stage, an improved lightweight MobileNetV3 framework is used to recognize the detect character regions, where the original Squeezeand-Excitation (SE) block is replaced by the efficient Shuffle Attention (SA) that integrates spatial and channel attention, improving the accuracy of Yi characters recognition. Meanwhile, the use of depth separable convolution and reversible residual structure can reduce the number of parameters and computation of the model, so that the model can better understand the contextual information and improve the accuracy of text recognition. The experimental results illustrate that the proposed method achieves good results in detecting and recognizing Yi characters, with detection and recognition accuracy rates of 97.5% and 96.8%, respectively. And also, we have compared the detection and recognition algorithms proposed in this paper with other typical algorithms. In these comparisons, the proposed model achieves better detection and recognition results with a certain reliability.

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“…Furthermore, it is worth noting that the ODConv exhibits a lower computational cost than standard convolution, as deduced from the computation of dhwk 3 C in C out according to Eq. (2).…”

Section: Yi Character Detection Based On Omni-dimensional Dynamic Con...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Method for Detecting and Recognizing Yi Character Based on Deep Learning

Sun,

Ding,

Sun

et al. 2024

CMC

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“…This clustered method is used as a rough classify method or global feature that reflects the structure significance of the characters (6) . (7) Proposed recognition system for Yi character using CNN with density-based clustering method and compared experiment results of different parameters, achieved good accuracy. The authors proposed (8) a system by using SIFT and HOG as feature extractor for recognizing handwritten character of Thai, Bengali and Latin and have succeeded good results.…”

Section: Introductionmentioning

confidence: 99%

OCR for historical Kannada documents using clustering methods

Ravi¹

2020

IJST

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Motivation: In India, the Language Kannada is an ancient and official language in Karnataka State. The study of ancient Kannada scripts from stone carvings, leaf, metal, cloth, paper and other sources enhances our knowledge on the traditions and culture practiced in Karnataka. Due to Poor Quality, variability and the contrast, the Kannada ancient scripts become very challenging to extract the information or to recognize the characters. Objectives: To design a suitable Optical Character Recognition (OCR) technique to read ancient Kannada scripts. Method: Clustering by fast search and find of density peaks is a state-of-the-art density-based clustering algorithm that can effectively find clusters with arbitrary shapes. However, it requires to calculate the distances between all the points in a data set to determine the density and separation of each point. Consequently, its computational cost is extremely high in the case of large-scale data sets. In this work the given document is preprocessed. The features alike SIFT and SURF are extracted and clustered using K-Means clustering. The similarity is computed using different measures. Findings: The classification accuracy was studied under different clustering methods like Kmeans, Agglomerative, Density based clustering with distance based measures like Euclidean and Manhattan. To evaluate the performance of the proposed method, we created our own database of Ashok, Kadamba, Hoysala and Mysuru scripts and experiment was conducted in a database of 4 classes under 70, 50 and 30 different training models from each class. Novelty: We propose a K-means clustering using SIFT and SURF for Kannada ancient manuscript.

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