NEOCR: A Configurable Dataset for Natural Image Text Recognition

Nagy, Róbert; Dicker, Anders; Meyer-Wegener, Klaus

doi:10.1007/978-3-642-29364-1_12

Cited by 36 publications

(17 citation statements)

References 11 publications

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“…There are more than 100 types of frequently-used languages all over the world, but a majority of the existing methods and benchmarks (except for [12,27,[79][80][81]) have focused on texts in English. In this age of globalization, it is urgent and indispensable to build systems that are able to handle multilingual texts and serve the people in the whole world.…”

Section: Discussionmentioning

confidence: 99%

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Scene text detection and recognition: recent advances and future trends

Zhu

Yao

Bai

2015

Front. Comput. Sci.

357

158

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Text, as one of the most influential inventions of humanity, has played an important role in human life, so far from ancient times. The rich and precise information embodied in text is very useful in a wide range of vision-based applications, therefore text detection and recognition in natural scenes have become important and active research topics in computer vision and document analysis. Especially in recent years, the community has seen a surge of research efforts and substantial progresses in these fields, though a variety of challenges (e.g. noise, blur, distortion, occlusion and variation) still remain. The purposes of this survey are three-fold: 1) introduce up-to-date works, 2) identify state-of-the-art algorithms, and 3) predict potential research directions in the future. Moreover, this paper provides comprehensive links to publicly available resources, including benchmark datasets, source codes, and online demos. In summary, this literature review can serve as a good reference for researchers in the areas of scene text detection and recognition.

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Section: Discussionmentioning

confidence: 99%

“…• NEOCR The NEOCR dataset 10) [79] includes images with multioriented texts in natural scenes. It contains 659 real world images with 5 238 annotated bounding boxes.…”

Section: Benchmark Datasets • Icdar 2003 and 2005mentioning

confidence: 99%

Scene text detection and recognition: recent advances and future trends

Zhu

Yao

Bai

2015

Front. Comput. Sci.

357

158

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“…The NEOCR dataset [33] contains 659 natural scene images with multi-oriented texts of high variability (see Figure 2c for examples). This database is intended for scene text recognition and provided multilingual evaluation environments, as it includes texts in eight European languages.…”

Section: Literature Reviewmentioning

confidence: 99%

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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“…For those tests, we used the "Word recognition" dataset from the ICDAR 2003 Competition [14] and the NEOCR dataset [15], using HCP or CPGS, the image was segmented and then for each segment the Tesseract OCR engine [16] was used to recognize the character.…”

Section: Characters Recognitionmentioning

confidence: 99%

Colour Perception Graph for Characters Segmentation

Berger

2014

Advances in Visual Computing

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Abstract. Characters recognition in natural images is a challenging problem, as it involves segmenting characters of various colours on various background. In this article, we present a method for segmenting images that use a colour perception graph. Our algorithm is inspired by graph cut segmentation techniques and it use an edge detection technique for filtering the graph before the graph-cut as well as merging segments as a final step. We also present both qualitative and quantitative results, which show that our algorithm perform at slightly better and faster to a state of the art algorithm.

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NEOCR: A Configurable Dataset for Natural Image Text Recognition

Cited by 36 publications

References 11 publications

Scene text detection and recognition: recent advances and future trends

Scene text detection and recognition: recent advances and future trends

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

Colour Perception Graph for Characters Segmentation

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