ICDAR 2015 competition on Robust Reading

Karatzas, Dìmosthenis; Gómez, Lluís; Nicolaou, Anguelos; Ghosh, Suman; Bagdanov, Andrew D.; Iwamura, Masatsugu; Matas, Jiřı́; Neumann, Lukáš; Chandrasekhar, Vijay; Lu, Shijian; Shafait, Faisal; Uchida, Seiichi; Valveny, Ernest

doi:10.1109/icdar.2015.7333942

Cited by 1,270 publications

(714 citation statements)

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“…It contains 28 short video sequences. An updated version of this dataset was provided in ICDAR 2015 [25] including a training set of 25 videos and a test set of 24 videos.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Figure 10 depicts the user interface of our evaluation tool as well as the precision and recall curves, where x-axis denotes t r values and y-axis denotes t p ones. The proposed performance metrics and their underlying constraints are similar to those used in ICDAR 2013 [24] and ICDAR 2015 [25] RRCs. It is worth noting that our annotation and evaluation tools are fully implemented in Java and are made open-source for standardization and validation purposes.…”

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confidence: 99%

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Open Datasets and Tools for Arabic Text Detection and Recognition in News Video Frames

et al. 2018

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Abstract:Recognizing texts in video is more complex than in other environments such as scanned documents. Video texts appear in various colors, unknown fonts and sizes, often affected by compression artifacts and low quality. In contrast to Latin texts, there are no publicly available datasets which cover all aspects of the Arabic Video OCR domain. This paper describes a new well-defined and annotated Arabic-Text-in-Video dataset called AcTiV 2.0. The dataset is dedicated especially to building and evaluating Arabic video text detection and recognition systems. AcTiV 2.0 contains 189 video clips serving as a raw material for creating 4063 key frames for the detection task and 10,415 cropped text images for the recognition task. AcTiV 2.0 is also distributed with its annotation and evaluation tools that are made open-source for standardization and validation purposes. This paper also reports on the evaluation of several systems tested under the proposed detection and recognition protocols.

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“…It contains 28 short video sequences. An updated version of this dataset was provided in ICDAR 2015 [25] including a training set of 25 videos and a test set of 24 videos.…”

Section: Literature Reviewmentioning

confidence: 99%

mentioning

confidence: 99%

Open Datasets and Tools for Arabic Text Detection and Recognition in News Video Frames

et al. 2018

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“…Although promising results have been achieved for scene text recognition [12], the datasets evaluated are not fully realistic -the text patterns are simple, the word orientations are only horizontal, they occupy a significant part of the image, and there is no perspective distortion or significant noise [19]. Therefore, these methods are not expected to perform well on the real In contrast, our system is designed to tackle those challenges from e-business images.…”

Section: Related Workmentioning

confidence: 99%

“…As a result, photo OCR techniques are required [1] [12]. However, most photo OCR methods were developed for text recognition in generic scenes, and have not been specifically designed for e-business image management.…”

Section: Introductionmentioning

confidence: 99%

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A Text Recognition and Retrieval System for e-Business Image Management

Zhou

McGuinness

O’Connor

2018

MultiMedia Modeling

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Abstract. The on-going growth of e-business has resulted in companies having to manage an ever increasing number of product, packaging and promotional images. Systems for indexing and retrieving such images are required in order to ensure image libraries can be managed and fully exploited as valuable business resources. In this paper, we explore the power of text recognition for e-business image management and propose an innovative system based on photo OCR. Photo OCR has been actively studied for scene text recognition but has not been exploited for e-business digital image management. Besides the well known difficulties in scene text recognition such as various size, location, orientation in text and cluttered background, e-business images typically feature text with extremely diverse fonts, and the characters are often artistically modified in shape, colour and arrangement. To address these challenges, our system takes advantage of the combinatorial power of deep neural networks and MSER processing. The cosine distance and n-gram vectors are used during retrieval for matching detected text to queries to provide tolerance to the inevitable transcription errors in text recognition. To evaluate our proposed system, we prepared a novel dataset designed specifically to reflect the challenges associated with text in e-business images. We compared our system with two other approaches for scene text recognition, and the results show our system outperforms other state-ofthe-art on the new challenging dataset. Our system demonstrates that recognizing text embedded in images can be hugely beneficial for digital asset management.

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