Scene Texts occur more frequently in most videos which may contain crucial information. Therninformation may have contents such as location and time. In Ethiopia most information on thernstreets are posted using Ethiopic (Geez) and Latin Scripts. In our Research work we have studiedrnMultilingual Text Detection, Script Identification and Character Recognition from Video Scenernusing Deep Learning Neural Network Model. rnThe Videos being captured by the digital camera are processed and Keyframes are extracted usingrnKeyframe Selection Algorithm, Text regions are detected by using Trained Convolutional NeuralrnNetwork and those text regions which are found by bounding box regression are cropped out byrntaking their bounding box values. The use of Faster R-CNN that consists of dropout layer for textrndetection has achieved a 91% of precision, 92.9% recall and an execution time of 7.5 sec duringrntesting the network. After taking those cropped text blocks, scripts are classified or identified byrnusing a trained network through transfer learning into their script classes. Following the scriptrnidentification Line Segmentation, Word segmentation and Character Segmentation usingrnHorizontal and Vertical Projection profile are performed which are the preprocessing steps forrnOptical Character Recognition, where script identification has achieved 88.5% of accuracy withoutrnthe use of dropout layer and 93.3% of accuracy with the use of dropout layer. The final phase ofrnthis work includes character recognition which lies on the previous text detection, and scriptrnidentification phases, different epochs were considered during training the network to maximizernthe efficiency of the network to recognize characters. The network that was trained with an epochrnsize of 200 has achieved 0.0076% of error during testing. This shows that maximizing the numberrnof epochs during setting the training options improves the character recognition performance whilerndecreasing the error value to the minimum value.