The 7th International Conference on Pre-modern Japanese Texts

くずし字を読む
エッジＡＩの開発
Development of Edge AI System
for Kuzushiji Recognition

早坂 太一（豊田工業高専）
Taichi HAYASAKA(National Institute of Technology, Toyota College)

Table of Contents
1. 深層学習によるくずし字認識
Recognition of Kuzushiji by Deep Learning.
2. エッジＡＩデバイスの開発
Development of Edge AI System.

3. クラウドＡＩとの連携（Future Works）
Cooperation between Cloud and Crowd Edge AI.

Introduction
Project to build an international collaborative research
network for pre-modern Japanese texts (NIJL-NW project)
was inaugurated at the National Institute of Japanese Literature in 2014, and it was decided toconvert approximately 300 thousand “Pre-modern Japanese Books” into
image data to be amalgamated with the bibliographic data base to produce “Database of Premodern Japanese Books”.

Too many books to interpret!
Please develop the ICT system to help us!
Japanese literature researchers

Problem formulation
Discrimination of Kuzushiji images to character types
represented by Unicode.

乃濃能農

廼

「の」
U+306E(ex) Classifying “の”in spite of different original Kanji characters.
↓Neural network approach (since 2015)

Why neural network approach?
 It may be possible to learn the difference in the characteristics ofKuzushiji (hand-written/prints, genres, regions, periods, etc).

→ no need to prepare a large database.
 Once the model is built, the time required for inference(recognition) is very short.
→ It can be realized as the application software operated even on
small computers.

Network architecture
18-Layered Convolutional Neural Network (CNN)

64x64 px

Layer Type

#UnitsFully-Connected

65535

Fully-Connected21024

Fully-Connected2,3

1024

Convolution1,2512

Convolution1,2256

Convolution1,2

128

Convolution1,2

64

1 w/ MaxPooling

2 w/ ReLU 3 w/ Dropout

Y. Zhang, “Deep convolutional network for handwritten Chinese character recognition”, CS231N course projectDatasets for Learning
WWW Download
CODH
Data
KanaKanji,Others
Total

#char
type

Manual Extraction

和翰名苑

ADEAC
Data

714,568

3,265

9,061

9,511

1,473

(96.82%)(0.44%)(1.23%)(1.29%)(0.20%)

17,0682,207(4.49%)(0.58%)

360,550(94.96%)

―

CODH
Data

五體字類―

1,075,118

3,265

26,12911,7181,473(96.19%)(0.29%)(2.34%)(1.05%)(0.13%)

4,299

48

1,638417

48

#char
type

Total/#char

737,878

77

9,583

379,8254,258

89

1,117,703

4,335

258

Total

Recognition Resultscompared with learning by CODH data only
100%

1.8%

1.8%

1st Cand.

80%
7.3%

60%
40%

96.9%

Conf.>10%
7.9%

96.9%
65.9%

73.9%

20%
0%
PrintsHand-Written

ＷＷＷ Application(http://vpac.toyota-ct.ac.jp)
When loading a document image and select one character by amouse or a finger, the recognition result is output immediately.
豊田高専 くずし字

Table of Contents
1. 深層学習によるくずし字認識
Recognition of Kuzushiji by Deep Learning.
2. エッジＡＩデバイスの開発
Development of Edge AI System.

3. クラウドＡＩとの連携（Future Works）
Cooperation between Cloud and Crowd Edge AI.

Development of standalone system forrecognition of Kuzushiji
 At present, there are not many schools and community facilities
that have the internet environment such as Wi-Fi that can be usedby a large number of people.
 We have been developing the standalone (edge AI) system bysmall computers for extracting and recognizing Kuzushiji.

“Kuzushiji recognizer” by cheap andsmall AI camera

Possible to recognize Hentaigana only.
(Recognition rate ≃ 75%)

“Kuzushiji recognizer” by Raspberry Pi
System that automatically extracts and recognizes Kuzushiji frompre-modern Japanese book images using Raspberry Pi, small andinexpensive single-board computers.

In the captured image, character areas
can be detected in about 5 seconds.
The network can recognize a Kuzushiji
character in about 0.08 seconds.

Evaluation(#Char.=939)

Results of extracting Precision 92.6%
 Recall 87.4%
 F-measure 89.9%(#Output Char.=887,#Accurate Char.=821)

Results of recognition
 Precision 76.3%
 Recall 72.1%
 F-measure 74.2%(#Accurate Char.=677)(Top 5 in certainty)

Table of Contents
1. 深層学習によるくずし字認識
Recognition of Kuzushiji by Deep Learning.
2. エッジＡＩデバイスの開発
Development of Edge AI System.

3. クラウドＡＩとの連携（Future Works）
Cooperation between Cloud and Crowd Edge AI.

Future works:

Cloud AI Approach by MQTT
PublishSubscribe

Input
ImagesInference
Results
Small
Computer

Edge
AI

CloudAI

Input
ImagesInference
Results

ServerSubscribe

Publish

Edge
AI
Edge
AIEdgeAI

Future works:

Cloud AI Approach by MQTTEdge
AIPublish
Edge
Edge
Edge AIAIAI
Edge
Edge
Edge AIAI
Edge
Edge
Edge AI
AI
AIAI
Edge
Edge
Edge AI
AI
Edge
Edge
Edge AIAIAIAI
Edge
Edge
Edge AIAIEdge
AIEdgeEdge
AIAIAIEdge
Edge
Edge AIAI
Edge

CloudAI

Input
ImagesInference
Results

ServerSubscribe

Semantic
Approach

SchemaCharacters

Neocognitron (Fukushima 1979)
can recognize Kuzushiji(Hayasaka, et al. 2015).

1st AI Boom

Ontology

2nd AI Boom

3rd AI Boom

Perceptron(1950s-70s)

MultiLayered
Perceptron(1980s-90s)

DeepLearning(2010s-)Possible to solve
simple problems.

Possible to solve
annoying problems.

Possible to solve
problems we cannot
think of answers (atsuper high speed).

…

あはめ「り二せまつしふすのまる。意に、味」ながにがSubscribe

Inference
Results

Small Computer

Inference
Results

EdgeAI

PublishOntology
SchemaCharacters

Characters

CloudAI

Server

The 7th International Conference on Pre-modern Japanese Texts

Thank you for your attention.
くずし字を読む
エッジＡＩの開発
Development of Edge AI Systemfor Kuzushiji Recognition
早坂 太一（豊田工業高専）
Taichi HAYASAKA(National Institute of Technology, Toyota College)