Using a Simple ACGAN for Manchu Script Dataset Supplementation

Poster presentation: 2023 Korea Multimedia Society Spring Conference

Abstract: The Manchu script is difficult to process with deep learning techniques in part because there is not much data widely available for neural network training. Therefore, this research trained a simple ACGAN for the purpose of supplementing an existing, small-scale Manch dataset. A dataset consisting of a total of 4,000 Manchu script letters was trained in an ACGAN for 30,000 steps. The resulting images produced by the generator model were sufficiently recognizable for dataset supplementation.

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1. Using a Simple ACGAN For Manchu Script Dataset Supplementation Aaron Snowberger • 이충호, 한밭대학교 2023.05.19 한국멀티미디어학회 춘계학술대회
2. Abstract 만주어 스크립트는 부분적으로 신경망 훈련에 널리 사용 가능한 데이터가 많지 않기 때문에 딥 러닝 기술로 처리하기 어렵다. 따라서 본 연구에서는 기존의 소규모 만주어 데이터 세트를 보완하기 위해 간단한 ACGAN을 훈련하였다. 총 4,000개의 만주 문자로 구성된 데이터 세트는 ACGAN에서 30,000 단계 동안 훈련되었다. 생성 모델에 의해 생성된 결과 이미지는 데이터 세트 보완을 위해 충분히 인식 가능하였다. The Manchu script is difficult to process with deep learning techniques in part because there is not much data widely available for neural network training. Therefore, this research trained a simple ACGAN for the purpose of supplementing an existing, small-scale Manch dataset. A dataset consisting of a total of 4,000 Manchu script letters was trained in an ACGAN for 30,000 steps. The resulting images produced by the generator model were sufficiently recognizable for dataset supplementation. Keywords: GAN | ACGAN | Manchu Script | Dataset Supplementation
3. Introduction Difficulties in Preprocessing Unavailability of Datasets Manchu script is written vertically, with every letter of a word connected by a central stem. This makes segmentation of letters difficult for pre-processing. Large datasets of Manchu script are not widely available for machine learning. This is the problem this research paper addresses.
4. Related Research Two preprocessing techniques Dataset in this Research Two main techniques for Manchu script recognition have been used. The first has been to attempt letter segmentation as a part of the pre-processing step[1,2], but this has proven difficult. The second has been to attempt segmentation-free recognition[3]. This research utilizes an existing, small-scale dataset of segmented Manchu script letters[4] with over a dozen different handwriting styles.
5. 01 02 03 Generator Discriminator ACGAN System Model & Methods Layers: • Input, dense, reshape • Conv2DTranspose x4 ◦ BatchNormalization ◦ ReLU • Filters: [128, 64, 32, 1] • Stride: 2 (last 2 layers: 1) • Kernel size: 5 Params: 1,332,161 trainable (704 untrainable) Layers: • Input • Conv2D x4 ◦ LeakyReLU ◦ alpha = 0.2 • Dense x3 • Activation layer • Filters: [32, 64, 128, 256] • Stride: 2 (last Conv: 1) • Kernel size: 5 Params: 1,605,638 trainable (0 untrainable) Training: • batch_size = 64 • latent_size = 100 • learning_rate = 2e-4 • decay = 6e-8 • RMSprop discriminator • Loss functions: ◦ Generator images: binary_crossentropy ◦ Discriminator predictions: categorical_crossentropy • Steps = 30,000
6. Step 15000 Step 20000 Step 25000 Step 30000 Step 500 Step 2500 Step 5000 Results Step 10000
7. Discussion & Conclusion • As the figures indicate, generated images became progressively more accurate over time. However, some graininess and noise can also be seen in some of the later images. This is not an error in the training of the GAN, but a representation of the dataset. Because ancient handwritten Manchu texts were scanned and cropped to create the training dataset, there was some noise present in some of the image backgrounds. • Therefore, in the creation of later Manchu datasets, a better image thresholding algorithm will be used to minimize the background noise. Nonetheless, the results of this ACGAN research have demonstrated the effectiveness of possibly supplementing small-scale Manchu datasets with generated images to bolster neural network training.
8. 1. G. Y. Zhang, J. J. Li, A. X. Wang, “A New Recognition Method for the Handwritten Manchu Character Unit,” in Proceedings of the Fifth International Conference on Machine Learning and Cybernetics, Dalian, China 2006, pp. 3339-3344, DOI: 10.1109/ICMLC.2006.258471. 2. A. Snowberger, C.H. Lee, “A New Segmentation and Extraction Method for Manchu Character Units,” in Proceedings for 2022 International Conference on Future Information and Communication Engineering, Jeju, South Korea, pp. 42-47, 2022. 3. R. Zheng, M. Li, J. He, J. Bi, and B. Wu, "Segmentation-Free Multi-Font Printed Manchu Word Recognition Using Deep Convolutional Features and Data Augmentation," in 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Beijing, China, 2018, pp. 1-6, DOI: 10.1109/CISP-BMEI.2018.8633208. 4. A. Snowberger, C.H. Lee, “A Simple MNIST Style Dataset and CNN Training for Manchu Script Characters,” in Proceedings of the 15th International Conference on Future Information & Communication Engineering, Jeju, South Korea 2023, p. 136-138. 5. R. Atienza, Advanced Deep Learning with TensorFlow 2 and Keras, 2nd ed., Packt Publishing Ltd., Birmingham, Feb. 2020. [Online] Available: https://www.packtpub.com/book/programming/9781838821654/. References