A Fine-Grained Approach to Scene Text Script Identification

Gómez, Lluís; Karatzas, Dìmosthenis

doi:10.1109/das.2016.64

Cited by 41 publications

(26 citation statements)

References 23 publications

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“…the ICDAR Robust Reading Competitions datasets (ICDAR2003 [48], IC-DAR2013 [49], and ICDAR2015 [4]), the Street View Text dataset (SVT) [50], and the Multi-Language end-to-end (MLe2e) dataset [51]. In all cases we provide results for their official test sets.…”

Section: Experiments and Resultsmentioning

confidence: 99%

TextProposals: A text-specific selective search algorithm for word spotting in the wild

Gómez

Karatzas

2017

Pattern Recognition

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Motivated by the success of powerful while expensive techniques to recognize words in a holistic way [1,2,3], object proposals techniques emerge as an alternative to the traditional text detectors. In this paper we introduce a novel object proposals method that is specifically designed for text. We rely on a similarity based region grouping algorithm that generates a hierarchy of word hypotheses. Over the nodes of this hierarchy it is possible to apply a holistic word recognition method in an efficient way.Our experiments demonstrate that the presented method is superior in its ability of producing good quality word proposals when compared with class-independent algorithms. We show impressive recall rates with a few thousand proposals in different standard benchmarks, including focused or incidental text datasets, and multi-language scenarios. Moreover, the combination of our object proposals with existing whole-word recognizers [2,3] shows competitive performance in end-to-end word spotting, and, in some benchmarks, outperforms previously published results. Concretely, in the challenging ICDAR2015 Incidental Text dataset, we overcome in more than 10 percent f-score the best-performing method in the last ICDAR Robust Reading Competition [4]. Source code of the complete end-to-end system is available at https://github.com/lluisgomez/TextProposals.

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Section: Experiments and Resultsmentioning

confidence: 99%

TextProposals: A text-specific selective search algorithm for word spotting in the wild

Gómez

Karatzas

2017

Pattern Recognition

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“…Experiments on CVSI dataset report an identification rate of more than 96%. Gomez et al [68] employed Naïve Bayes classifier with convolutional features to identify script in unconstrained scene text. The work was later extended to apply patch-based classification using CNNs [69].…”

Section: Script Recognitionmentioning

confidence: 99%

Detection and recognition of cursive text from video frames

Mirza

Zeshan

Atif

et al. 2020

J Image Video Proc.

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Textual content appearing in videos represents an interesting index for semantic retrieval of videos (from archives), generation of alerts (live streams), as well as high level applications like opinion mining and content summarization. The key components of such systems require detection and recognition of textual content which also make the subject of our study. This paper presents a comprehensive framework for detection and recognition of textual content in video frames. More specifically, we target cursive scripts taking Urdu text as a case study. Detection of textual regions in video frames is carried out by fine-tuning deep neural networks based object detectors for the specific case of text detection. Script of the detected textual content is identified using convoluational neural networks (CNNs), while for recognition, we propose a UrduNet, a combination of CNNs and long short-term memory (LSTM) networks. A benchmark dataset containing cursive text with more than 13,000 video frame is also developed. A comprehensive series of experiments is carried out reporting an F-measure of 88.3% for detection while a recognition rate of 87%.

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“…The latest work on Latin or English using Convolutional neural Networks (ConvNets) was recently proposed by [61]. They proposed a multi-stage approach for script identification.…”

Section: Classification Techniques For Scene Text Recognitionmentioning

confidence: 99%

“…The detailed feature extraction procedure using ConvNets is very much applicable to complex data like Arabic whether presented as a printed, scene text or as handwritten form. The latest work on Latin or English using ConvNets was recently proposed by [61]. They proposed a multi-stage approach for script identification.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

See 1 more Smart Citation

Arabic Cursive Text Recognition from Natural Scene Images

Ahmed

Naz²,

Razzak

et al. 2019

Applied Sciences

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This paper presents a comprehensive survey on Arabic cursive scene text recognition. The recent years’ publications in this field have witnessed the interest shift of document image analysis researchers from recognition of optical characters to recognition of characters appearing in natural images. Scene text recognition is a challenging problem due to the text having variations in font styles, size, alignment, orientation, reflection, illumination change, blurriness and complex background. Among cursive scripts, Arabic scene text recognition is contemplated as a more challenging problem due to joined writing, same character variations, a large number of ligatures, the number of baselines, etc. Surveys on the Latin and Chinese script-based scene text recognition system can be found, but the Arabic like scene text recognition problem is yet to be addressed in detail. In this manuscript, a description is provided to highlight some of the latest techniques presented for text classification. The presented techniques following a deep learning architecture are equally suitable for the development of Arabic cursive scene text recognition systems. The issues pertaining to text localization and feature extraction are also presented. Moreover, this article emphasizes the importance of having benchmark cursive scene text dataset. Based on the discussion, future directions are outlined, some of which may provide insight about cursive scene text to researchers.

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A Fine-Grained Approach to Scene Text Script Identification

Cited by 41 publications

References 23 publications

TextProposals: A text-specific selective search algorithm for word spotting in the wild

TextProposals: A text-specific selective search algorithm for word spotting in the wild

Detection and recognition of cursive text from video frames

Arabic Cursive Text Recognition from Natural Scene Images

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