Extracting and Segmenting Container Name from Container Images

Chowdhury, Murad; Deb, Kaushik

doi:10.5120/13001-0039

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…Recognizing the text in images of scenes can be useful for a wide range of computer vision applications, e.g. robot navigation or industry automation [11,25]. Traditionally, handcrafted features (e.g.…”

Section: Related Workmentioning

confidence: 99%

Running Event Visualization using Videos from Multiple Cameras

Napolean

Wibowo

Gemert

2019

Proceedings Proceedings of the 2nd International Workshop on Multimedia Content Analysis in Sports

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Visualizing the trajectory of multiple runners with videos collected at different points in a race could be useful for sports performance analysis. The videos and the trajectories can also aid in athlete health monitoring. While the runners unique ID and their appearance are distinct, the task is not straightforward because the video data does not contain explicit information as to which runners appear in each of the videos. There is no direct supervision of the model in tracking athletes, only filtering steps to remove irrelevant detections. Other factors of concern include occlusion of runners and harsh illumination. To this end, we identify two methods for runner identification at different points of the event, for determining their trajectory. One is scene text detection which recognizes the runners by detecting a unique 'bib number' attached to their clothes and the other is person re-identification which detects the runners based on their appearance. We train our method without ground truth but to evaluate the proposed methods, we create a ground truth database which consists of video and frame interval information where the runners appear. The videos in the dataset was recorded by nine cameras at different locations during the a marathon event. This data is annotated with bib numbers of runners appearing in each video. The bib numbers of runners known to occur in the frame are used to filter irrelevant text and numbers detected. Except for this filtering step, no supervisory signal is used. The experimental evidence shows that the scene text recognition method achieves an F1-score of 74. Combining the two methods, that is -using samples collected by text spotter to train the reidentification model yields a higher F1-score of 85.8. Re-training the person re-identification model with identified inliers yields a slight improvement in performance(F1 score of 87.8). This combination of text recognition and person re-identification can be used in conjunction with video metadata to visualize running events.

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Section: Related Workmentioning

confidence: 99%

Running Event Visualization using Videos from Multiple Cameras

Napolean

Wibowo

Gemert

2019

Proceedings Proceedings of the 2nd International Workshop on Multimedia Content Analysis in Sports

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“…In modern society, text plays a more important role than ever before as an essential tool for communication and collaboration. Meanwhile, scene text reading has become an active research area due to its wide applications in the real world, such as image instant translation [8,39], image search [36,41], and industrial automation [1,12].…”

Section: Introductionmentioning

confidence: 99%

Optimal Boxes: Boosting End-to-End Scene Text Recognition by Adjusting Annotated Bounding Boxes via Reinforcement Learning

Tang¹,

Wenming²,

Song³

et al. 2022

Preprint

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Text detection and recognition are essential components of a modern OCR system. Most OCR approaches attempt to obtain accurate bounding boxes of text at the detection stage, which is used as the input of the text recognition stage. We observe that when using tight text bounding boxes as input, a text recognizer frequently fails to achieve optimal performance due to the inconsistency between bounding boxes and deep representations of text recognition. In this paper, we propose Box Adjuster, a reinforcement learning-based method for adjusting the shape of each text bounding box to make it more compatible with text recognition models. Additionally, when dealing with cross-domain problems such as synthetic-to-real, the proposed method significantly reduces mismatches in domain distribution between the source and target domains. Experiments demonstrate that the performance of end-toend text recognition systems can be improved when using the adjusted bounding boxes as the ground truths for training. Specifically, on several benchmark datasets for scene text understanding, the proposed method outperforms state-of-the-art text spotters by an average of 2.0% F-Score on end-to-end text recognition tasks and 4.6% F-Score on domain adaptation tasks.

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“…Text contains high-level semantic information that could be beneficial for analyzing and understanding the world around us. Reading text in scene images can be used in many real world applications such as image retrieval, automatic navigation and industrial automation [1]- [4]. As a key component of scene text reading, scene text detection has received increasing attention in computer vision.…”

Section: Introductionmentioning

confidence: 99%

Instance Segmentation Network With Self-Distillation for Scene Text Detection

Yang

Gong

et al. 2020

IEEE Access

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Segmentation based methods have become the mainstream for detecting scene text with arbitrary orientations and shapes. In order to address challenging problems such as separating the text instances that are very close to each other, however, these methods often require time-consuming postprocessing. In this paper, we propose an instance segmentation network (ISNet), which simultaneously generates prototype masks and per-instance mask coefficients. After linearly combining the two components, ISNet can implement fast text location. Furthermore, we apply self-distillation to train the ISNet and refine its detection accuracy. We have evaluated the proposed method on four popular benchmarks, i.e., ICDAR2015, ICDAR2017 MLT, CTW1500 and Total-Text, and the experimental results show that it can achieve better tradeoff between accuracy and efficiency for scene text detection.

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Extracting and Segmenting Container Name from Container Images

Cited by 7 publications

References 11 publications

Running Event Visualization using Videos from Multiple Cameras

Running Event Visualization using Videos from Multiple Cameras

Optimal Boxes: Boosting End-to-End Scene Text Recognition by Adjusting Annotated Bounding Boxes via Reinforcement Learning

Instance Segmentation Network With Self-Distillation for Scene Text Detection

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