480 research outputs found
Text Localization in Video Using Multiscale Weber's Local Descriptor
In this paper, we propose a novel approach for detecting the text present in
videos and scene images based on the Multiscale Weber's Local Descriptor
(MWLD). Given an input video, the shots are identified and the key frames are
extracted based on their spatio-temporal relationship. From each key frame, we
detect the local region information using WLD with different radius and
neighborhood relationship of pixel values and hence obtained intensity enhanced
key frames at multiple scales. These multiscale WLD key frames are merged
together and then the horizontal gradients are computed using morphological
operations. The obtained results are then binarized and the false positives are
eliminated based on geometrical properties. Finally, we employ connected
component analysis and morphological dilation operation to determine the text
regions that aids in text localization. The experimental results obtained on
publicly available standard Hua, Horizontal-1 and Horizontal-2 video dataset
illustrate that the proposed method can accurately detect and localize texts of
various sizes, fonts and colors in videos.Comment: IEEE SPICES, 201
Recognizing Degraded Handwritten Characters
In this paper, Slavonic manuscripts from the 11th
century written in Glagolitic script are
investigated. State-of-the-art optical character recognition methods produce poor results
for degraded handwritten document images. This is largely due to a lack of suitable
results from basic pre-processing steps such as binarization and image segmentation.
Therefore, a new, binarization-free approach will be presented that is independent of
pre-processing deficiencies. It additionally incorporates local information in order to
recognize also fragmented or faded characters. The proposed algorithm consists of
two steps: character classification and character localization. Firstly scale invariant
feature transform features are extracted and classified using support vector machines.
On this basis interest points are clustered according to their spatial information. Then,
characters are localized and eventually recognized by a weighted voting scheme of
pre-classified local descriptors. Preliminary results show that the proposed system can
handle highly degraded manuscript images with background noise, e.g. stains, tears,
and faded characters
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
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