A New Morphology-based Method for Text Detection in Image and Video

Elkhattabi, Zaynab; Tabii, Youness; Benkaddour, Abdelhamid

doi:10.5120/18131-9251

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…In order to determine whether the found part contains text or not, an algorithm was developed to check the structure of the part. [17]. In a similar study, text regions of document images are identified based on the differences between opening and closing operations [18].…”

Section: Literature Reviewmentioning

confidence: 99%

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End to End Invoice Processing Application Based on Key Fields Extraction

Arslan

2022

IEEE Access

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In this paper, an automatic invoice processing system, which is in great demand among private and public companies, was proposed. The proposed system supports all invoice file types that can be submitted by companies. Companies can easily submit invoices to the system via the web interface or email, and all invoices submitted to the system are queued and processed sequentially. If the invoice is a text file, the invoice information is extracted from the text by using template matching. If the invoice is an image, the text and table areas are detected and extracted. For table detection, we used both image processing based and YOLOv5-based deep learning method. Cell extraction was then performed from the extracted table images. As a result of these processes, all text and table cells were obtained as images and these images were converted into machine-readable text using the open-source software Tesseract OCR. Tesseract already provides trained models for English and Turkish. However, these models do not provide successful results for invoices submitted by companies in Turkish. Therefore, the new fine-tuned model trained with invoices in Turkish was used for OCR. The experimental results showed that the trained Turkish model was more accurate than the Turkish and English models provided by Tesseract. In addition, the YOLOv5-based table detection model was more accurate than the image-processing-based table detection method.

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Section: Literature Reviewmentioning

confidence: 99%

“…• The area of the region must be of a specific size to eliminate small contours in the invoice. • The aspect ratio, width, and height of the region must be within a certain range for it to be accepted as text or a table [17], [36]. • Within the bounding box of the image shown in Fig.…”

Section: ) Table and Text Detection With Image Processingmentioning

confidence: 99%

End to End Invoice Processing Application Based on Key Fields Extraction

Arslan

2022

IEEE Access

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“…VS is being used in security surveillance systems to detect and analyze suspicious or anomalous activities. Personal VS is used to share occasional videos on social media, generate sports highlights, make trailers of movies and serials and video content indexing to facilitate fast browsing of huge video through a video search engine, and so on [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

An Effective Video Summarization Framework Based on the Object of Interest Using Deep Learning

Haq

Asif

Ahmad

et al. 2022

Mathematical Problems in Engineering

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The advancements in digital video technology have empowered video surveillance to play a vital role in ensuring security and safety. Public and private enterprises use surveillance systems to monitor and analyze daily activities. Consequently, a massive volume of data is generated in videos that require further processing to achieve security protocol. Analyzing video content is tedious and a time-consuming task. Moreover, it also requires high-speed computing hardware. The video summarization concept has emerged to overcome these limitations. This paper presents a customized video summarization framework based on deep learning. The proposed framework enables a user to summarize the videos according to the Object of Interest (OoI), for example, person, airplane, mobile phone, bike, and car. Various experiments are conducted to evaluate the performance of the proposed framework on the video summarization (VSUMM) dataset, title-based video summarization (TVSum) dataset, and own dataset. The accuracy of VSUMM, TVSum, and own dataset is 99.6%, 99.9%, and 99.2%, respectively. A desktop application is also developed to help the user summarize the video based on the OoI.

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Multilingual Scene Text Detection Using Gradient Morphology

Dhar

Chakraborty

Choudhury

et al. 2020

International Journal of Computer Vision and Image Processing

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Text detection in natural scene images is an interesting problem in the field of information retrieval. Several methods have been proposed over the past few decades for scene text detection. However, the robustness and efficiency of these methods are downgraded due to high sensitivity towards various complexities of an image. Also, in multi-lingual environment where texts may occur in multiple languages, a method may not be suitable for detecting scene texts in certain languages. To counter these challenges, a gradient morphology-based method is proposed in this paper that proves to be robust against image complexities and efficiently detects scene texts irrespective of their languages. The method is validated using low quality images from standard multi-lingual datasets like MSRA-TD500 and MLe2e. The performance of the method is compared with that of some state-of-the-art methods, and comparably better results are observed.

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A New Morphology-based Method for Text Detection in Image and Video

Cited by 3 publications

References 9 publications

End to End Invoice Processing Application Based on Key Fields Extraction

End to End Invoice Processing Application Based on Key Fields Extraction

An Effective Video Summarization Framework Based on the Object of Interest Using Deep Learning

Multilingual Scene Text Detection Using Gradient Morphology

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