Localizing text regions in images taken from natural scenes is one of the challenging problems dueto variations in font, size, color and orientation of text. In this paper, we introduce a new concept socalled Edge Color Signature for localizing text regions in an image. This method is able to localizeboth Farsi and English texts. In the proposed method rst a pyramid using diff...

In this paper we propose a summarization approach, nicknamed AZOM, that combines statistical and conceptual property of text and in regards of document structure, extracts the summary of text. AZOM is also capable of summarizing unstructured documents. Proposed approach is localized for Persian language but easily can apply to other languages. The empirical results show comparatively superior r...

In Text Information Extraction (TIE) process, the text regions are localized and extracted from the images. It is an active research problem in computer vision applications. Diversity in text is due to the differences in size, style, orientation, alignment of text, low image contrast and complex backgrounds. The semantic information provided by an image can be used in different applications suc...

Recognition of Arabic-like scripts such as Persian and Urdu is more challenging than Latin-based scripts. This due to the presence a two-dimensional structure, context-dependent character shapes, spaces overlaps, placement diacritics. We present an attention based encoder-decoder model that learns read handwritten text in context. A novel localization penalty introduced encourage attend only on...

Text localization is the first step when automatically reading text in images. Since existing methods often fail when applied to unconstrained images, in this paper we propose a more robust approach exploiting different kind of information. In particular, we first extract textural features, a combination of a stroke filter with a super-pixel segmentation, and then search for connected component...

The detection and recognition of text from unconstrained, general-purpose video is an important research problem with multiple applications in the surveillance, archiving and content-based retrieval contexts. Many text detection and localization algorithms have been proposed in the literature. However many of these algorithms either make simplistic assumptions as to the nature of the text to be...

Video-Text Pre-training (VTP) aims to learn transferable representations for various downstream tasks from large-scale web videos. To date, almost all existing VTP methods are limited retrieval-based tasks, e.g., video retrieval, whereas their transfer potentials on localization-based temporal grounding, under-explored. In this paper, we experimentally analyze and demonstrate the incompatibilit...