Text Normalization Framework for Handwritten Cursive Languages by Detection and Straightness the Writing Baseline

The process of baseline detection has an important role in optical recognition systems and document image analysis systems. It is widely used in many various preprocessing stages as a text normalization including skew, slant and slop corrections, writing lines straightness and characters segmentation, as well as in feature extraction process. In this work, a new framework for baseline detection and straightness for cursive handwritten texts is proposed based on analysis and extraction the directions features from the subwords of the text skeleton. Arabic script is chosen as a case study since it is cursive and widely adopted in many languages all around the world such as Arabic, Jawi, Urdu and Persian. The experiments results on a popular Arabic dataset showed the efficiency of the proposed framework.