Character Segmentation from Handwritten Gujarati isolated words using Deep Learning

Information retrieval from scanned handwritten digital copies is a very challenging task especially in Indian scripts like Gujarati due to the presence of joint and conjuct characters as well as matras, cursive nature and varying size of the characters. There are two methods namely recognition-based and recognition-free for document image retrieval. The difference in both approaches lies in the level of segmentation. There are two levels of segmentation namely Fine and Coarse Grain. In Fine-Grain segmentation, the base character and the matras are considered as separate and are two different units of segmentation. In Coarse-Grain segmentation, the base character and matras are considered as a single unit of segmentation. The accuracy of the segmentation highly affects the result of information retrieval. The research here heads towards addressing these issues. Deep learning has been very effective in many domains but has not been used much in this domain. In this research, we propose a Coarse Grain segmentation method using the object detection model Faster RCNN and a Fine Grain segmentation method using a combination of Connected Component Analysis and Faster RCNN. The annotation of the dataset for training these models has been carried out manually using LabelImg tool.