Handwritten recognition systems take input from the paper documents scanner, images; touch screen, and, electronic pen. After processing it offers the output as a electronic document suitable for subsequent access and manipulation. There are a lot of handwritten documents. Which are damaged due to dust, flooding, fire, and natural disaster like an earthquake. Nowadays there are a lot of documents putting on shelves for many years and it takes more space it is bulky to manage and edit a majority of data available in a handwritten form. This requires an approach to change the documents into electronic form for easy of searching and retrieval as per users’ need. In this research work, we have presented the development of offline handwritten character recognition model for Awngi documents. We focus on the 26 base characters Awngi language. The proposed character recognition model contains all of the essential steps that are obligatory for developing an efficient recognition system. The designed model includes modules like preprocessing, segmentation, feature extraction, and classification. In the preprocessing stage, image resizes grayscale, noise reduction, morphological transformation, and binarization. In any handwritten character recognition system is separating individual characters from the document. Better character segmentation phase, has achieved using dual thresholding criterion to minimize the character segmentation error. A CNN is used for feature extraction character classification purposes. Furthermore, In this paper, we prepared a new public image dataset for Awngi handwritten characters a dataset containing a total of 30,115 out of the total dataset images,80%of dataset is used for training, 20% dataset is used for testing. We collected data from injibara teacher’s education, injibara baunk primary school students and Amhara mass media from Awngi media. We are interested in the new success of end-to-end learning in pattern recognition we propose a new trained end-to-end fashion model. The experimental results on handwritten Awngi character recognition that our model achieves a training accuracy of 96.6% and 92.6% of the testing accuracy of the proposed model.