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.
Using a Simple ACGAN
For Manchu Script
Aaron Snowberger • 이충호, 한밭대학교
2023.05.19 한국멀티미디어학회 춘계학술대회
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
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
GAN | ACGAN | Manchu Script | Dataset Supplementation
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.
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.
This research utilizes an existing, small-scale
dataset of segmented Manchu script letters
with over a dozen diﬀerent handwriting styles.
01 02 03
Generator Discriminator ACGAN
System Model & Methods
● Input, dense, reshape
● Conv2DTranspose x4
● Filters: [128, 64, 32, 1]
● Stride: 2 (last 2 layers: 1)
● Kernel size: 5
Params: 1,332,161 trainable
● Conv2D x4
○ 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
● batch_size = 64
● latent_size = 100
● learning_rate = 2e-4
● decay = 6e-8
● RMSprop discriminator
● Loss functions:
○ Generator images:
○ Discriminator predictions:
● Steps = 30,000
Step 15000 Step 20000 Step 25000 Step 30000
Step 500 Step 2500 Step 5000
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.
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