Text detection and recognition is a hot topic for researchers in the field of image processing. It gives attention to Content based Image Retrieval (CBIR) community in order to fill the semantic gap between low level and high level features. Several methods have been developed for text detection and extraction that achieve reasonable accuracy for natural scene text (camera images) as well as mu...

-This paper presents a new scheme for character detection and segmentation from natural scene images. In the detection stage, stroke edge is employed to detect possible text regions, and some geometrical features are used to filter out obvious non-text regions. Moreover, in order to combine unary properties with pairwise features into one framework, a graph model of candidate text regions is se...

In this paper, we develop a novel unified framework called DeepText for text region proposal generation and text detection in natural images via a fully convolutional neural network (CNN). First, we propose the inception region proposal network (InceptionRPN) and design a set of text characteristic prior bounding boxes to achieve high word recall with only hundred level candidate proposals. Nex...

The  present  study  aimed  at  investigating  DIF  sources  on  an  EFL  reading  comprehension test.  Accordingly,  2  DIF  detection  methods,  logistic  regression  (LR)  and  item  response theory  (IRT),  were  used  to  flag  emergent  DIF  of  203  (110  females  &  93  males)  Iranian EFL examinees’ performance on a reading comprehension test. Seven hypothetical DIF sources were examin...

Text detection and recognition in video is challenging due to the presence of different types of texts, namely, graphics (video caption), scene (natural text), 2D, 3D, static and dynamic texts. Developing a universal method that works well for all these types is hard. In this paper, we propose a novel method for classifying graphics-scene and 2D-3D texts in video to enhance text detection and r...