Matches in SemOpenAlex for { <https://semopenalex.org/work/W4385695429> ?p ?o ?g. }
Showing items 1 to 77 of
77
with 100 items per page.
- W4385695429 abstract "A common biometric for establishing a person's identity in document forensics is verification of handwritten signatures. Signature plays an important role in banking, financial, commercial and so on. However, issues associated with signatures are numerous since any two signatures may look very similar with little to no differences written by the same person. Despite the enormous research efforts, offline signature verification is still difficult, especially when attempting to differentiate between competent forgeries and authentic signatures because the visual differences between the two can sometimes be less noticeable than between two authentic ones. Due to the expanding usage of digital signatures and electronic documents, there has been an increase in the demand for effective and precise signature forgery detection systems (SFDS). In order to effectively combat handwritten signature forgery, this study intends to create an automated system utilizing a deep neural network (DNN) especially a simple and efficient Convolutional Neural Network (CNN) that can determine if a given signature is real or faked. The model will take signature image as input and extract important features through different layers of CNN that will helpful in distinguishing genuine and forged signatures. The model performance is also accessed with different image resizing techniques and optimizers used in training of CNN. The model's performance in terms of a set of assessment metrics, such as accuracy, precision, recall, and Fl-score are provided in this paper after extensive tests are conducted across a huge dataset of handwritten signatures including both genuine and various types of forged signatures. Additionally, the model's performance is also contrasted with a cutting-edge deep learning model. The simulation results conclusively demonstrate the appropriateness of the suggested methodology for automatically detecting forgery in handwritten signature images." @default.
- W4385695429 created "2023-08-10" @default.
- W4385695429 creator A5008063377 @default.
- W4385695429 creator A5067460865 @default.
- W4385695429 creator A5092617827 @default.
- W4385695429 creator A5092617828 @default.
- W4385695429 creator A5092617829 @default.
- W4385695429 date "2023-06-09" @default.
- W4385695429 modified "2023-09-25" @default.
- W4385695429 title "Handwritten signature forgery detection using Deep Neural Network" @default.
- W4385695429 cites W2898714651 @default.
- W4385695429 cites W2901465038 @default.
- W4385695429 cites W2977063634 @default.
- W4385695429 cites W3017312119 @default.
- W4385695429 cites W3153582567 @default.
- W4385695429 cites W4283739660 @default.
- W4385695429 cites W4306377436 @default.
- W4385695429 doi "https://doi.org/10.1109/apsit58554.2023.10201777" @default.
- W4385695429 hasPublicationYear "2023" @default.
- W4385695429 type Work @default.
- W4385695429 citedByCount "0" @default.
- W4385695429 crossrefType "proceedings-article" @default.
- W4385695429 hasAuthorship W4385695429A5008063377 @default.
- W4385695429 hasAuthorship W4385695429A5067460865 @default.
- W4385695429 hasAuthorship W4385695429A5092617827 @default.
- W4385695429 hasAuthorship W4385695429A5092617828 @default.
- W4385695429 hasAuthorship W4385695429A5092617829 @default.
- W4385695429 hasConcept C112640561 @default.
- W4385695429 hasConcept C118463975 @default.
- W4385695429 hasConcept C153180895 @default.
- W4385695429 hasConcept C154945302 @default.
- W4385695429 hasConcept C177264268 @default.
- W4385695429 hasConcept C184297639 @default.
- W4385695429 hasConcept C199360897 @default.
- W4385695429 hasConcept C2524010 @default.
- W4385695429 hasConcept C2779386606 @default.
- W4385695429 hasConcept C2779696439 @default.
- W4385695429 hasConcept C33923547 @default.
- W4385695429 hasConcept C38652104 @default.
- W4385695429 hasConcept C41008148 @default.
- W4385695429 hasConcept C50644808 @default.
- W4385695429 hasConcept C52622490 @default.
- W4385695429 hasConcept C81363708 @default.
- W4385695429 hasConcept C99138194 @default.
- W4385695429 hasConceptScore W4385695429C112640561 @default.
- W4385695429 hasConceptScore W4385695429C118463975 @default.
- W4385695429 hasConceptScore W4385695429C153180895 @default.
- W4385695429 hasConceptScore W4385695429C154945302 @default.
- W4385695429 hasConceptScore W4385695429C177264268 @default.
- W4385695429 hasConceptScore W4385695429C184297639 @default.
- W4385695429 hasConceptScore W4385695429C199360897 @default.
- W4385695429 hasConceptScore W4385695429C2524010 @default.
- W4385695429 hasConceptScore W4385695429C2779386606 @default.
- W4385695429 hasConceptScore W4385695429C2779696439 @default.
- W4385695429 hasConceptScore W4385695429C33923547 @default.
- W4385695429 hasConceptScore W4385695429C38652104 @default.
- W4385695429 hasConceptScore W4385695429C41008148 @default.
- W4385695429 hasConceptScore W4385695429C50644808 @default.
- W4385695429 hasConceptScore W4385695429C52622490 @default.
- W4385695429 hasConceptScore W4385695429C81363708 @default.
- W4385695429 hasConceptScore W4385695429C99138194 @default.
- W4385695429 hasLocation W43856954291 @default.
- W4385695429 hasOpenAccess W4385695429 @default.
- W4385695429 hasPrimaryLocation W43856954291 @default.
- W4385695429 hasRelatedWork W1909427334 @default.
- W4385695429 hasRelatedWork W1987664611 @default.
- W4385695429 hasRelatedWork W2086669062 @default.
- W4385695429 hasRelatedWork W2122438338 @default.
- W4385695429 hasRelatedWork W2137744223 @default.
- W4385695429 hasRelatedWork W2390264616 @default.
- W4385695429 hasRelatedWork W2888573161 @default.
- W4385695429 hasRelatedWork W2943834649 @default.
- W4385695429 hasRelatedWork W3099602952 @default.
- W4385695429 hasRelatedWork W3122954453 @default.
- W4385695429 isParatext "false" @default.
- W4385695429 isRetracted "false" @default.
- W4385695429 workType "article" @default.