Text non-text classification based on area occupancy of equidistant pixels
Tauseef Khan, Ayatullah Faruk Mollah · Procedia Computer Science · 2020
Text detection and localization from text embedded natural images is still considered as a challenging problem in complex imagery environment. Foreground object segmentation followed by classification is a popular approach for this task. Component level object classification in clutter environment is therefore an important sub-problem. Appropriate extraction of foreground objects leads to effective classification that may certainly increase the performance of text detection. In this paper, a novel feature vector is developed based on area occupancy profile of equidistant pixels is reported for text/non-text classification. The generated feature descriptors are script invariant and much effective in practical scenario. This proposed feature set is evaluated on our dataset using five different pattern classifiers and experimental result shows that the said feature set yields more than 86% classification accuracy irrespective of scripts.