Improving Open-Vocabulary Scene Text Recognition

Feild, Jacqueline; Learned-Miller, Erik

doi:10.1109/icdar.2013.125

Cited by 22 publications

(14 citation statements)

References 22 publications

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“…In [15], an approach based on mathematical morphological operations has been studied for extraction of Devanagari and Bengali texts from scene images. Recent works like [16], [17] and [18] adopt more advanced techniques to get better text segmentation performance. In [16], stroke width transform [19] and some heuristic rules are used to get foreground and background seed pixels.…”

Section: Segmentation Based Approachmentioning

confidence: 99%

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Multilingual scene character recognition with co-occurrence of histogram of oriented gradients

Tian

Bhattacharya

et al. 2016

Pattern Recognition

138

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Section: Segmentation Based Approachmentioning

confidence: 99%

“…By combining with medial pixels and the symmetric properties of character stroke, it could restore the broken parts on the inner and outer contour of the character. In [18] [22] to identify the best-quality text candidates from a set of stable regions based on measures to evaluate the text probability.…”

Section: Segmentation Based Approachmentioning

confidence: 99%

Multilingual scene character recognition with co-occurrence of histogram of oriented gradients

Tian

Bhattacharya

et al. 2016

Pattern Recognition

138

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“…A system for open-vocabulary text recognition in images of natural scenes was presented in [4]. Bilateral regression segmentation was introduced to segment images into foreground text and background.…”

Section: Existing Text Recognition Methodsmentioning

confidence: 99%

“…This modified technique was used to produce likelihood maps for every text character. In second stage, word-formation cost function and computed likelihood maps were used to detect and recognize the text in natural images.A system for open-vocabulary text recognition in images of natural scenes was presented in [4]. Bilateral regression segmentation was introduced to segment images into foreground text and background.…”

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confidence: 99%

Text Recognition from Complex Colored Images using Neural Network with Discriminative Feature Extraction

Devi¹,

Sathyanarayanan²,

Sumathi³

2017

IJCA

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The objective of this paper is to project a new methodology for text recognition from the features of segmented text component of images. Text classification algorithm is the main decision making stage of text recognition system. Artificial neural network approach has been used to train and test the character based on the extracted features. Finally, the identified texts are converted in to readable/editable version of text file. KeywordsText extraction, Feature extraction,Text Recognition, Neural Network, Back Propagation. . INTRODUCTIONThe aim of text recognition is to recognize and covert human readable text image characters to machine readable characters. Classification stage is the main decision making stage of text recognition system and uses the features extracted in the previous stage to identify the text component according to the features extracted. . EXISTING TEXT RECOGNITION METHODSText recognition stage is the main decision making stage of text recognition system. Various classifiers techniques are proposed in the literature and are used for the recognition of text. Some of them are multi-level slice classifier, minimum distance classifier, maximum likelihood classifier, fuzzy measure, artificial neural network, support vector machines, decision tree etc.A robust method [1] that uses convolutional co-occurrence histogram of oriented gradient (ConvCoHOG) and discriminative than both the histogram of oriented gradient (HOG) and the co-occurrence histogram of oriented gradients (CoHOG).An image was first divided into smaller patches and feature extraction procedure was applied in every patch separately to extract features. The orientation of gradient of each pixel within a patch is then quantized into histogram bins and then, normalized histogram was concatenated together to form a feature vector ant it was trained by al linear SVM classifier.In end-to-end method [2] individual characters were detected as Extremal Regions. The regions were first agglomerated into text lines by an efficient pruned exhaustive search that estimates the text direction on each triplet of regions and the constraints induced by the text direction contribute to the similarity measure used for clustering. In the next stage, each region in the text line was labeled by the character recognition module, which was trained on synthetic fonts. Regions with low confidence were rejected, which eliminates clutter regions that were included in the text line formation stage. In the last step, a directed graph was constructed with corresponding scores assigned to each node and edge, the scores were normalized by width of the area that they represent and a standard dynamic programming algorithm was used to select the path with the highest score. The sequence of regions and their labels induced by the optimal path was the output of the method.Gokhan Yildirim et.al [3] proposed a technique to detect and recognize text in a unified manner by searching for words directly without reducing the image into text regions or individual charact...

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“…Character Identification: We use the bilateral regression [2] for character identification. However, our approach is different than the original method in that we only use it to estimate the horizontal location of each character in word image.…”

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confidence: 99%

Exploiting Color Information for Better Scene Text Recognition

Fraz¹,

Sarfraz²,

Edirisinghe³

2014

Proceedings of the British Machine Vision Conference 2014

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The problem of scene text recognition has gained significant importance because of its numerous applications. A variety of methods has been recently proposed that explore various theoretical and practical aspects to solve this problem. In this work, we focus towards a framework to recognize the text present in outdoor scene images. The text information carries one important property, that is, its colour in comparison to its background. Text information is always placed in such a way that it stands out from its background. In the same way, most of the time the characters in a word possess similar colour that helps us to recognize the letters of a particular word. We exploit this characteristic of text regions to solve the problem of character recognition. The character recognition pipeline is further extended in to a word recognition framework where the estimated word combinations are matched against a lexicon.The existing approaches for scene text recognition can be roughly divided in to two broad categories: Region grouping based methods and object recognition based methods. In this work, we have combined region grouping method with object recognition based strategy to achieve the advantages of both techniques. First, we binarize the image using colour information and perform foreground segmentation to separate characters from background. Next, we extract shape representation features on binary images and perform character classification using a pre-trained classifier. The recognized characters form words that are fed in to a string similarity matching stage where lexicon based search is performed to find the closest matching word.Character Identification: We use the bilateral regression [2] for character identification. However, our approach is different than the original method in that we only use it to estimate the horizontal location of each character in word image. The bilateral regression models the foreground pixels by using a weighted regression that assigns weight to each pixel according to its location with respect to foreground in feature space. The pixels that belong to the foreground get high weights in comparison to the pixels belonging to background. In this case, the regression model in equation 1 represents the quadratic surface that best models the image as a function of pixel locations.We enhance the operation of bilateral regression by a pre-processing step where the foreground colour is estimated a priori. We apply n-level colour quantization to achieve binary image for each quantization level. We use Minimum Variance Quantization (MVQ) originally proposed by Heckbert [3]. We quantize each word image in to three colours and analyse the respective binary maps for three quantization levels to estimate the foreground. The characters are cropped from the actual word images using the estimated horizontal location and width from bilateral regression while the height is kept same as the height of the actual word image. the segmented masks are used to crop the characters from original (coloured) im...

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Improving Open-Vocabulary Scene Text Recognition

Cited by 22 publications

References 22 publications

Multilingual scene character recognition with co-occurrence of histogram of oriented gradients

Multilingual scene character recognition with co-occurrence of histogram of oriented gradients

Text Recognition from Complex Colored Images using Neural Network with Discriminative Feature Extraction

Exploiting Color Information for Better Scene Text Recognition

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