Recognition of Characters from Streaming Videos

Non

Text Recognition Past, Present and Future

Text recognition in various images is a research domain which attempts to develop a computer programs with a feature to read the text from images by the computer. Thus there is a need of character recognition mechanisms which results Document Image Analysis (DIA) which changes different documents in paper format computer generated electronic format. In this paper we have read and analyzed various methods for text recognition from different types of text images like scene images, text images, born digital images and text from videos. Text Recognition is an easy task for people who can read, but to make a computer that does character recognition is highly difficult task. The reasons behind this might be variability, abstraction and absence of various hard-and-fast rules that locate the appearance of a visual character in various text images. Therefore rules that is to be applied need to be very heuristically deduced from samples domain. This paper gives a review for various existing methods. The objective of this paper is to give a summary on well-known methods

Text detection in natural scenes through weighted majority voting of DCT high pass filters, line removal, and color consistency filtering

Detecting text in images presents the unique challenge of finding both in-scene and superimposed text of various sizes, fonts, colors, and textures in complex backgrounds. The goal of this system is not to recognize specific letters or words but only to determine if a pixel is text or not. This pixel level decision is made by applying a set of weighted classifiers created using a set of high pass filters, and a series of image processing techniques. It is our assertion that the learned weighted combination of frequency filters in conjunction with image processing techniques may show better pixel level text detection performance in terms of precision, recall, and f-metric, than any of the components do individually. Qualitatively, our algorithm performs well and shows promising results. Quantitative numbers are not as high as is desired, but not unreasonable. For the complete ensemble, the f-metric was found to be 0.36

Text Detection and Extraction from VideoÂ

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Text extraction in natural scenes using region-based method

Text in images is a very important clue for image indexing and retrieving. Unfortunately, it is a challenging work to accurately and robustly extract text from a complex background image. In this paper, a novel region-based text extraction method is proposed. In doing so, the candidate text regions are detected by 8-connected objects detection algorithm based on the edge image. Then the non-text regions are filtered out using shape, texture and stroke width rules. Finally, the remaining regions are grouped into text lines. Since stroke width is the intrinsic and particular characteristics of the text, the accuracy of the non-text filter are notably promoted. The improved Stroke Width Transform in the paper is less computing complexities and more accurate. Experimental results on sample ICDAR competition Dataset and our dataset show that the proposed method has the best performance compared with other five methods

System for caption text extraction on a hierarchical region-based image representation

English: This work presents a technique for detecting caption text for indexing purposes. This technique is to be included in a generic indexing system dealing with other semantic concepts. The various object detection algorithms are required to share a common image description which is a hierarchical region-based image model. Caption text objects are detected combining texture and geometric features, which are estimated using wavelet analysis and taking advantage of the region-based image model, respectively. Analysis of the region hierarchy provides the final caption text objects

Video metadata extraction in a videoMail system

Currently the world swiftly adapts to visual communication. Online services like YouTube and Vine show that video is no longer the domain of broadcast television only. Video is used for different purposes like entertainment, information, education or communication. The rapid growth of today’s video archives with sparsely available editorial data creates a big problem of its retrieval. The humans see a video like a complex interplay of cognitive concepts. As a result there is a need to build a bridge between numeric values and semantic concepts. This establishes a connection that will facilitate videos’ retrieval by humans. The critical aspect of this bridge is video annotation. The process could be done manually or automatically. Manual annotation is very tedious, subjective and expensive. Therefore automatic annotation is being actively studied. In this thesis we focus on the multimedia content automatic annotation. Namely the use of analysis techniques for information retrieval allowing to automatically extract metadata from video in a videomail system. Furthermore the identification of text, people, actions, spaces, objects, including animals and plants. Hence it will be possible to align multimedia content with the text presented in the email message and the creation of applications for semantic video database indexing and retrieving