Text localization and detection method for born-digital images

Abstract

Multimedia data has increased rapidly in recent years. Textual information present in multimedia contains important information about the image/video content. The proposed method provides very efficient way to extract text from Born-Digital images. Firstly, edges are extracted from a grayscale image. New edge detection technique is introduced in this research, which gives better results for low-contrast web images. Then morphological operators are applied on the image. These operators are used to connect the broken edges of objects present in an image. Each object is classified as text or non-text on the basis of text features such as size, height to width ratio, and binary transitions, using K-Means clustering. Two new features, namely horizontal fluctuation count and vertical fluctuation count, are introduced in the proposed work. Dataset of International Conference on Document Analysis and Recognition 2011 Robust Reading Competition, Challenge 1: "Reading Text in Born-Digital Images (Web and Email)" is used in this research. The proposed method performed best in the above-mentioned competition.

Fuzzy‐Based Segmentation for Variable Font‐Sized Text Extraction from Images/Videos

“…In order to prove the practicability of the proposed segmentation method, fuzzy merging is added as the post segmentation process in textorter [37], which is the best technique in ICDAR Robust Reading Competition 2011 [38], whereby the results justify a major improvement in the detection rate of textorter. It is also factual that many isolated characters are not detected as text by textorter, as these are not merged as a complete word.…”

Semantic Text Detection in Born-Digital Images via Fully Convolutional Networks

“…This survey focuses on the classical approaches which are not based on deep learning. We note that much fewer works have focused on born-digital images [4], [9], [10], [11]. Those works -for born-digital images -all follow a traditional non deep learning-based approaches, and they rely on finding candidate (initial) components or interest regions as a first step.…”