Prof. Yanzhi Guan Profile

Prof. Yanzhi Guan

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Proceedings Article | 27 March 2024 Paper

Image stitching based on scanned images of super large format artworks

Yanzhi Guan, Jinchuan Peng, Xiang Chen, Longlong Tian

Proceedings Volume 13105, 131050N (2024) https://doi.org/10.1117/12.3026649

KEYWORDS: Feature extraction, Image processing, Deep learning

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For ancient paintings and calligraphies, including thangkas and murals, which are often of large format and cannot be scanned at one time, it is necessary to utilize high-precision CIS image sensors to scan the paintings and calligraphies several times in segments, and then finally get the finished images through image stitching. Among them, fast and accurate stitching of large format images with hundreds of millions of pixels is a major difficulty. Due to the huge size, more computational resources are required, and conventional image stitching algorithms can not directly complete the stitching task in the case of limited performance. Therefore, we propose a high-resolution image stitching algorithm OR-SIFT based on deep learning prediction of overlapping regions, which combines the convolutional neural network with the traditional feature detection method. It uses the neural network to predict the overlapping region as the region of interest for subsequent stitching algorithms. Then, an improved SIFT algorithm is used to extract and describe features in the overlapping region, followed by precise stitching. Additionally, a strategy for continuously stitching multiple high-resolution images is proposed to reduce computational complexity while ensuring accuracy, achieving continuous stitching of multiple high-resolution images.

Proceedings Article | 16 August 2023 Paper

Text error correction after text recognition based on MacBERT4CSC

Yanzhi Guan, Ziliang Pang, Yehui Ding, Longlong Tian

Proceedings Volume 12787, 127872K (2023) https://doi.org/10.1117/12.3004939

KEYWORDS: Error control coding, Error analysis, Optical character recognition, Statistical modeling, Data modeling, Image processing, Target recognition, Semantics, Model-based design, Education and training

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With the wide application of text recognition technology, the text recognition model's own limitations and environmental factors interfere with the recognition error rate is high. To address the above situation, a text error correction method based on MacBERT4CSC after text recognition is proposed. Firstly, using the single-word text recognition confidence, we obtain the suspected wrong words and positions by setting thresholds for error detection of the text to be corrected, and then iterate through the constructed confusion set for text error correction in priority, and after the traversal is completed, the MacBERT4CSC model recalls the candidate words with suspected wrong positions, and finally, after the similarity of the word code and the MacBERT4CSC model score meet the set conditions, The error correction is finally completed by sorting the candidate words that meet the conditions. By imitating the error type of word recognition to build the dataset for word recognition and calling Ali's word recognition API for word recognition to obtain the test set to be corrected, the comparison experiments show that the MacBERT4CSC recall and word bar code sorting method improves in accuracy, recall rate and F1 value compared with other deep models and traditional rule error correction methods, which verifies the effectiveness of the method.

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