C. Teixeira Samuel, Kalita Jahnabi, Wang Huayuan, Qin Weiwei, Shi Yingying, Xia Mingrong, Kumar Deepak, Yang Miaomiao, Páramo-Pérez Itzel, Elizabeth Reyes-Martínez Juana, Chandra Remya, S. Lopes Daiana, SIngh Ramandeep, Padilla-Vaca Felipe, Tamayo-Nuñez Jessica, Anaya-Velázquez Fernando, de Melo Rodrigues Veridiana, Chetia Dipak, Idalia Vargas-Maya Naurú, Li Gai, Franco Bernardo, Zhang Jiewen, Sun Ruihua, Liliana España-Sánchez Beatríz, Zhang Haohan, Zhao Jing, Abdizadeh Tooba, Vijayan Dileep, N.C. Gimenes Sarah, Sun Yajing, Banerjee Rintu, de la Mora Javier, Abdizadeh Rahman, Jiménez-Charris Eliécer, Rangel-Serrano Ángeles, Ma Limin, Solano-Redondo Luis, Rudrapal Mithun, Hadizadeh Farzin, and Montealegre-Sánchez Leonel
Background: A novel method to detect the text region from the natural image using the discriminative deep feature of text regions is presented with deep learning concept in this manuscript. Objective: Curve Text Detection (CTD) from the natural image is generally based on two different tasks: learning of text data and text region detection. In the learning of text data, the goal is to train the system with a sample of letters and natural images, while, in the text region detection, the aim is to confirm whether the detected regions are text region or not. The emphasis of this research is on the development of deep learning algorithm. Methods: A novel approach has been proposed to detect the text region from natural images which simultaneously tackles three combined challenges: 1) pre-processing of the image without losing text region; 2) appropriate segmentation of text region using their strokes, and 3) training of data. In pre-processing, image enhancement and binarization are done then morphological operations are defined with the Maximally Stable Extremal Region (MSER) based segmentation technique which operates on the basis of stroke region of text and then finds out the (Speed Up Robust Feature) SURF key point from those regions. Results: Based on the SURF feature, text region is detected from the images using a trained structure of Artificial Neural Network (ANN) which is based on deep learning mechanism. Conclusion: CTW-1500 dataset is used to simulate the proposed work and the parameters like Precision, Recall, F-Measure (H-mean), Execution time, Accuracy and Error Rate are computed and are compared with the existing work to depict the effectiveness of the work.