Authors: Kai Yang, Ye Wang, Bin Wang, Wentao Liu, Xiaolu Ding, Jun Zhu, Ming Chen, Peng Yao, Zhixin Qiu

Affiliation: CCB Financial Technology Co. Ltd, China

Description: 1. Data Analysis
This competition provided 5000 pieces of training data officially. Upon analyzing the data, we found that it can be classified into four categories: round, oval, square, and triangular, with the round and oval categories being the primary ones. The training set contains various conditions, including multi-directional rotations, uneven colors, overlapping seals, and indistinct seal patterns.
2. Data Processing
When it comes to data analysis, we began by re-annotating the training set images and enlarging them to squares. We then rotated the data and produced a total of 15,000 images. Data generation was carried out on difficult samples, including those with overlapping or blurry stamps. Prior to generating the seal data, we gathered a large number of company and organization names from the internet. Then, we generated the rotation angle and position of each individual character based on its length and merged them into the seal's background image. Moreover, we output the coordinates of the outer edge points of the text. To create a more realistic representation of seals in the generated data, we incorporated various colors, fonts, backgrounds, and textures. The base image for each seal was created by randomly cropping backgrounds, and we used RGBA format during data generation to allow for control over the color depth of the seal by adding a transparency channel. We also included two types of seal borders: solid and fragmented.
3. Model Introduction
In this segmentation task, we employed a “voting ensemble” method to detect the content of the seal title. Five models are utilized in the method, namely Mask R-CNN, K-Net, Segformer, Segmenter, and UperNet. Each model generates a mask. And we utilize a majority vote to derive the final mask, which allows us to identify the seal title area on the mask.

method: INTIME_OCR2023-03-21

Authors: Wei Wang, Chengxiang Ran, Jin Wei, Xinye Yang, Tianjiao Cao, Fangmin Zhao, Yu Zhou

Affiliation: Institute of Information Engineering, Chinese Academy of Sciences; Mashang Consumer Finance Co., Ltd

Description: One stage detector based on bezier regression, trained with 10k synthtic seal images and train set. The synthtic images are generated by taking WTW dataset( as background images and Chinese Company-Names-Corpus( as corpus.

method: SPDB LAB2023-03-16

Authors: Jie Li 、Wei Wang、Yuqi Zhang、Ruixue Zhang、Yiru Zhao、Danya Zhou、Di Wang、Dong Xiang、Hui Wang、Min Xu、Pengyu Chen、Bin Zhang、Chao Li、Shiyu Hu、Songtao Li、Yunxin Yang

Affiliation: Shanghai Pudong Development Bank


Description: Circle seals, ellipse seals, rectangle seals and triangle seals were trained with different method in task 1. The seal title detection model is trained using the provided training data and the synthetic data in the team, and the detection model is PANNet.The synthetic data is based on the style analysis of training data, and more than 20,000 training samples are synthesized in total.Two different PANNet models based on Circle and ellipse seals,rectangle and triangle seals are trained respectively for test set testing.

Ranking Table

Description Paper Source Code
2023-03-19Dao Xianghu light of TianQuan99.06%99.06%99.06%99.92%99.92%99.92%
2023-03-16SPDB LAB97.60%97.60%97.60%99.92%99.92%99.92%
2023-03-21DB with SegFormer98.11%95.42%96.75%99.65%96.92%98.27%
2023-03-21DB with SegFormer97.37%95.68%96.52%99.17%97.44%98.30%
2023-03-16DB with SegFormer97.72%95.34%96.52%99.61%97.18%98.38%
2023-03-21DB with SegFormer97.04%95.70%96.36%99.01%97.64%98.32%
2023-03-21AppAI for Seal 96.00%96.00%96.00%99.76%99.76%99.76%
2023-03-21DB with SegFormer95.95%95.78%95.87%98.00%97.82%97.91%
2023-03-19PAN++ with Res10192.22%92.22%92.22%97.48%97.48%97.48%
2023-03-13Seal Detect88.32%88.32%88.32%99.48%99.48%99.48%
2023-03-08Keypoint-based curved text detection82.42%82.42%82.42%92.72%92.72%92.72%
2023-03-20Seal Detect82.28%82.28%82.28%99.50%99.50%99.50%
2023-03-17Seal Detect80.84%80.84%80.84%99.62%99.62%99.62%
2023-03-21AppAI for Seal76.68%76.68%76.68%99.48%99.48%99.48%
2023-03-20AppAI for Seal64.62%64.62%64.62%99.54%99.54%99.54%
2023-03-07Seal Detect61.40%61.40%61.40%97.28%97.28%97.28%
2023-03-19DB-based segmtntation model831.05%31.06%31.06%98.96%98.98%98.97%
2023-03-16DB-based segmtntation model30.04%30.04%30.04%99.04%99.04%99.04%
2023-03-18DB-based segmtntation model30.03%30.04%30.04%99.02%99.04%99.03%
2023-03-09base method (DBNet_resnet18_fpn)13.94%13.94%13.94%88.60%88.60%88.60%
2023-03-13AppAI for Seal10.54%10.54%10.54%98.02%98.02%98.02%
2023-03-20AppAI for Seal8.22%8.22%8.22%98.60%98.60%98.60%
2023-03-20Mask way1.28%1.28%1.28%4.30%4.30%4.30%

Ranking Graphic

Ranking Graphic