World Journal of Engineering Research and Technology (WJERT) has indexed with various reputed international bodies like : Google Scholar , Index Copernicus , Indian Science Publications , SOCOLAR, China , International Institute of Organized Research (I2OR) , Cosmos Impact Factor , Research Bible, Fuchu, Tokyo. JAPAN , Scientific Indexing Services (SIS) , Jour Informatics (Under Process) , UDLedge Science Citation Index , International Impact Factor Services , International Scientific Indexing, UAE , International Society for Research Activity (ISRA) Journal Impact Factor (JIF) , International Innovative Journal Impact Factor (IIJIF) , Science Library Index, Dubai, United Arab Emirates , Scientific Journal Impact Factor (SJIF) , Science Library Index, Dubai, United Arab Emirates , Eurasian Scientific Journal Index (ESJI) , Global Impact Factor (0.342) , IFSIJ Measure of Journal Quality , Web of Science Group (Under Process) , Directory of Research Journals Indexing , Scholar Article Journal Index (SAJI) , International Scientific Indexing ( ISI ) , Scope Database , Academia , 

World Journal of Engineering Research and Technology

( An ISO 9001:2015 Certified International Journal )

An International Peer Reviewed Journal for Engineering Research and Technology

An Official Publication of Society for Advance Healthcare Research (Reg. No. : 01/01/01/31674/16)

ISSN 2454-695X

Impact Factor : 7.029

ICV : 79.45

News & Updation

  • Article Invited for Publication

    Article are invited for publication in WJERT Coming Issue

  • ICV

    WJERT Rank with Index Copernicus Value 79.45 due to high reputation at International Level

  • New Issue Published

    Its Our pleasure to inform you that, WJERT April 2024 Issue has been Published, Kindly check it on


    APRIL 2024 Issue has been successfully launched on 1 APRIL 2024.

  • WJERT New Impact Factor

    WJERT Impact Factor has been Increased to 7.029 for Year 2024.




Nkolika O. Nwazor* and Babangida Bakoji


Financial institutions are faced with the menace of fraud committed by criminals who forge the signatures of their customers on bank documents. This work is on the development of a handwritten text recognition system using deep neural network. Offline handwritten text recognition technique was used in this work. The code for the model was written in Keras, a backend for TensorFlow used typically as an application programmable interface (API) for building deep learning model. The system was trained using the handwritten text of one of the authors. The performance of the training model was improved by changing the learning rate, the dropout threshold, the batch size, and the number of iterations (hyper parameter tuning). The accuracy of the system in predicting handwritten text was found to be 0.9806 i.e. 98.06%. An application was developed for the fraud detection. The system was evaluated using a written text it was not trained with and it was able to detect it as a fraud.

[Full Text Article] [Download Certificate]