A Robust Authentication System Handwritten Documents using Local Features for Writer Identification

Kamal, Parves; Rahman, Faisal; Mustafiz, Saad

doi:10.5626/jcse.2014.8.1.11

Cited by 3 publications

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[24] leverages online writing information and deep CNNs on 187 writers with Chinese page input and on 134 writers with English page input on CASIA Handwriting Database. On the research of recognition on character level, Kamal Parves et al [12] introduce an approach with the idea of splitting number of small sub-images based on a local feature extracted by writer-specific characteristics. Bensefia Ameur et al [2] present an original approach based on the analysis of a unique sample of a handwriting word by using the Levenshtein edit distance based on Fisher-Wagner algorithm.…”

Section: Introductionmentioning

confidence: 99%

Handwritten Style Recognition for Chinese Characters on HCL2020 Dataset

et al. 2020

Pattern Recognition and Computer Vision

View full text Add to dashboard Cite

Structural features of Chinese characters provide abundant style information for handwritten style recognition, while prior work on this task has few senses of using structural information. Meanwhile, based on current handwritten Chinese character datasets, it is hard to obtain a good generalization model only by character category and writer information. Therefore, we add the structural information known as morpheme which is the smallest and unique structure in Chinese character into the large handwritten dataset HCL2000 and update it to HCL2020. We also present a deep fusion network (Morpheme-based Handwritten Style Recognition Network, M-HSRNet), capturing both overall layout characteristics and detail structural features of characters to recognize handwritten style. The evaluation results of the proposed model on HCL2020 are observed to prove the effectiveness of morpheme. Together with the proposed Morpheme Encoder module, our approach achieves an accuracy of 78.06% in handwritten style recognition, which is 3 points higher than the result without morpheme information.

show abstract

Section: Introductionmentioning

confidence: 99%