Name
Abstract | ||
|---|---|---|
Unsegmented text recognition is a crucial component in financial document processing systems. Financial text materials, such as receipts, transcripts, identification documents, etc. often involve critical personal information. In many circumstances, these data reside in protected servers of different institutions and must not be transferred beyond the institutional firewall. The emerging technology of Federated Learning (FL) provides a data-secure way of uniting isolated datasets in model training. Using the FL framework, text recognition models can be trained with larger collection of image samples.In previous works, federated text recognition models only deal with single-character images and alpha-numeric corpus. Such models are not competent in industrial applications, especially in Chinese text recognition problems. In this paper, we apply federated learning with a deep convolutional network to perform variable-length text string recognition with a large corpus.In our experiments, we compared two prevalent federated learning frameworks, namely, Tensorfiow Federated and PySyft. Results show that federated text recognition models can achieve similar or even higher accuracy than models trained on deep learning framework. On a 5-client distributed dataset, the best character accuracy is achieved by TFF at 49.20%. Extensive experiments are also conducted to evaluate the effect of distributed data storage over the performance of trained models. TFF again achieved a maximum character precision of 54.33% with non-distributed dataset. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICTAI.2019.00186 | 2019 IEEE 31ST INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI 2019) |
Keywords | Field | DocType |
Federated learning, federated optimization, deep learning, text recognition, optical character recognition | Firewall (construction),Information retrieval,Computer science,Document processing,Distributed data store,Server,Optical character recognition,Emerging technologies,Artificial intelligence,Personally identifiable information,Deep learning,Machine learning | Conference |
ISSN | Citations | PageRank |
1082-3409 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xinghua Zhu | 1 | 0 | 2.70 |
| Jianzong Wang | 2 | 61 | 34.65 |
| Zhenhou Hong | 3 | 0 | 1.01 |
| Tian Xia | 4 | 0 | 0.34 |
| Jing Xiao | 5 | 7 | 5.78 |