Multi-book Label Detection Model using Object Detection and OCR

Kim, Seongyeon; Park, Jin-Woo; Kim, Sang-Min; Na, Yung; Jang, Yujin

doi:10.14801/jkiit.2023.21.2.1

Cited by 1 publication

(1 citation statement)

References 6 publications

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“…Hyeong et al [5] used EasyOCR to recognize text from Material Safety Data Sheets (MSDS). Kim et al [16] aimed to identify text on the side covers of books placed on bookshelves by employing the Google Cloud Vision API. Lastly, Moon et al [6] developed a solution to recognize handwritten letters penned by children for educational purposes.…”

Section: Related Workmentioning

confidence: 99%

Gradual OCR: An Effective OCR Approach Based on Gradual Detection of Texts

Park,

Shin

2023

Mathematics

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In this paper, we present a novel approach to optical character recognition that incorporates various supplementary techniques, including the gradual detection of texts and gradual filtering of inaccurately recognized texts. To minimize false negatives, we attempt to detect all text by incrementally lowering the relevant thresholds. To mitigate false positives, we implement a novel filtering method that dynamically adjusts based on the confidence levels of recognized texts and their corresponding detection thresholds. Additionally, we use straightforward yet effective strategies to enhance the optical character recognition accuracy and speed, such as upscaling, link refinement, perspective transformation, the merging of cropped images, and simple autoregression. Given our focus on Korean chart data, we compile a mix of real-world and artificial Korean chart datasets for experimentation. Our experimental results show that our approach outperforms Tesseract by approximately 7 to 15 times and EasyOCR by 3 to 5 times in accuracy, as measured using a Jaccard similarity-based error rate on our datasets.

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Section: Related Workmentioning

confidence: 99%