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Recognition of characters in natural images is a challenging task due to the complex background, variations of text size and perspective distortion, etc. Traditional optical character recognition (OCR) engine cannot perform well on those unconstrained text images. A novel technique is proposed in this paper that makes use of convolutional co occurrence histogram of oriented gradient (ConvCoHOG), which...
Recognition of curved text in natural scene image is a challenging task. Due to complex background and unpredictable characteristics of scene text and noise, text characters in strings are often touching that affects the performance of segmentation and recognition. This paper presents a novel approach for curved text recognition using Hidden Markov Models (HMM). From curved text, a path of sliding...
This paper presents a method for temporal integration, which can be used to improve the recognition accuracy of video texts. Given a word detected in a video frame, we use a combination of Stroke Width Transform and SIFT (Scale Invariant Feature Transform) to track it both backward and forward in time. The text instances within the word's frame span are then extracted and aligned at pixel level. In...
Scene text recognition is a fundamental step in End-to-End applications where traditional optical character recognition (OCR) systems often fail to produce satisfactory results. This paper proposes a technique that uses co-occurrence histogram of oriented gradients (Co-HOG) to recognize the text in scenes. Compared with histogram of oriented gradients (HOG), Co-HOG is a more powerful tool that captures...
This paper presents a two-stage method for multi-oriented video character segmentation. Words segmented from video text lines are considered for character segmentation in the present work. Words can contain isolated or non-touching characters, as well as touching characters. Therefore, the character segmentation problem can be viewed as a two stage problem. In the first stage, text cluster is identified...
Character shape reconstruction for the scene character is challenging and interesting because scene character usually suffers from uneven illumination, complex background, perspective distortion. To address such ill conditions, we propose to utilize Histogram Gradient Division (HGD) and Reverse Gradient Orientation (RGO) to select Candidate Text Pixels (CTPs) for a given input character. Ring Radius...
Achieving good character recognition rate in video images is not as easy as achieving the same from the scanned documents because of low resolution and complex background in video images. In this paper, we propose a new method using fusion of horizontal, vertical and diagonal information obtained by the wavelet and the gradient on text line images to enhance the text information. We apply k-means...
In this paper, we propose a method based on gradient vector flow for video character segmentation. By formulating character segmentation as a minimum cost path finding problem, the proposed method allows curved segmentation paths and thus it is able to segment overlapping characters and touching characters due to low contrast and complex background. Gradient vector flow is used in a new way to identify...
The current OCR cannot segment words and characters from video images due to complex background as well as low resolution of video images. To have better accuracy, this paper presents a new gradient based method for words and character segmentation from text line of any orientation in video frames for recognition. We propose a Max-Min clustering concept to obtain text cluster from the normalized absolute...
This paper presents a new method based on Fourier and moments features to extract words and characters from a video text line in any direction for recognition. Unlike existing methods which output the entire text line to the ensuing recognition algorithm, the proposed method obtains each extracted character from the text line as input to the recognition algorithm because the background of a single...
With large databases of document images available,a method for users to find keywords in documents will be useful. One approach is to perform Optical Character Recognition (OCR) on each document followed by indexing of the resulting text. However, if the quality of the document is poor or time is critical,complete OCR of all images is infeasible. This paper build upon previous works on Word Shape...
A common problem encountered in signboard recognition is the perspective distortion of characters. In this paper, we propose a method which is able to directly recognize characters under severe perspective distortion without perspective rectification. In this method, a character is represented by a sequence of cross ratio spectra, in which the perspective effect can be modeled as an one-dimensional...
This paper presents a document retrieval technique that is capable of searching document images without optical character recognition (OCR). The proposed technique retrieves document images by a new word shape coding scheme, which captures the document content through annotating each word image by a word shape code. In particular, we annotate word images by using a set of topological shape features...
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