特記事項
データ公開の関係上、元のコンペティションの提供データよりも、明治期以降刊行資料の一部（学習用105枚、評価用52枚）が少なくなっています。

背景
図書館が所蔵資料をデジタル化することは、資料の検索可能性や提供可能性を向上させる上で重要な意味を持ちます。デジタル化は、多くの場合テキスト情報を持たない画像データの作製にとどまっており、そのままでは資料本文中に記述されている情報を検索することができませんが、昨今のOCR技術の進展により、画像データからテキストデータを作成することで本文検索が可能になるため、技術的検討が進められています。例えば、国立国会図書館ではOCRを利用した本文検索機能を含む、機械学習技術を応用した実験的検索サービス「次世代デジタルライブラリー(https://lab.ndl.go.jp/dl/)」が公開されています。しかし、昭和前期以前に刊行された資料の画像は、撮影時の資料の状態や現代の刊行物とのレイアウトの違いといった問題から、新刊書等をOCR処理する場合に比べてテキスト化の精度が大きく低下するという課題があります。このような課題を解決しOCR処理の精度の向上を図るため、処理を行う前段階で、画像データのレイアウト認識を行いテキスト領域や資料画像データを対象とした研究等に有用な領域を特定するアルゴリズムの作成に挑戦していただきます。

課題
資料画像に対して、予測対象のレイアウトラベルを含む矩形領域を、bounding box =(x1, y1, x2, y2) として割り当て、且つラベルの1つを付与していただきます。各画像には、1つ以上の bounding boxが割り当てられます。bounding box は画像の左上を原点（0,0）とし、オブジェクト領域の左上の座標（x1, y1）、右下の座標（x2, y2）の4つを指定することで表現されます。なお、予測対象となるレイアウトラベルは、「古典籍資料」と「明治期以降刊行資料」で異なります。

データセット

内訳
学習用データセット評価用データセット
古典籍資料1219枚211枚
明治期以降刊行資料1070枚200枚
合計2289枚411枚

※本データセットは国立国会図書館が作成・公開しているレイアウトデータセット(https://github.com/ndl-lab/layout-dataset)をもとに、「国立国会図書館刊行物」のデータセット及びモデル性能評価用データを追加したものです。

構成
・資料画像（jpeg画像）
・アノテーションデータ（資料の種別やデータ公開可否、メタ情報（著者名や出版年等）、出現するレイアウトラベル・矩形タグ領域）（json形式）
　※評価用データセットには、出現するレイアウトラベル・矩形タグ領域は含まれません。

古典籍資料
特徴
明治期より前に出版された出版物であり、浮世絵や和書・漢籍資料が含まれる。また、マイクロ資料を再デジタル化した場合など、強いノイズの乗った資料も存在する。浮世絵の中に文字が書き込まれているなど、複数のレイアウトの重なりがある場合が多い。

含まれるレイアウトラベル

ラベル名説明
1_overall資料範囲全体
2_handwrittenくずし字の文字ライン
3_typographyくずし字以外の文字ライン
4_illustrationイラスト（写真含む）
5_stamp印影（蔵書印等）

画像サンプル


明治期以降刊行資料
特徴
明治期以降に出版された、冊子の形態をとる出版物である。マイクロ資料をデジタル化した資料など、強いノイズの乗った資料が多く存在する。多くは昭和前期より前に刊行された資料であるが、一部戦後に刊行された刊行物を含む。

含まれるレイアウトラベル

ラベル名説明
1_overall資料範囲全体
4_illustrationイラスト（写真含む）
5_stamp印影（蔵書印等）
6_headline見出し
7_caption図表見出し
8_textline6_headline, 7_caption 以外の文字ライン
9_table表
※ラベル名の先頭の数字は両資料で通し番号。

画像サンプル

評価対象（予測対象）となるレイアウトラベル

ラベル名説明
1_overall資料範囲全体
2_handwrittenくずし字の文字ライン
3_typographyくずし字以外の文字ライン
4_illustrationイラスト（写真含む）
5_stamp印影（蔵書印等）
6_headline見出し
7_caption図表見出し
8_textline6_headline, 7_caption 以外の文字ライン
※ラベル「9_table（表）」は、学習用データセットには含まれますが、評価対象（予測対象）からは外します。

評価関数
・評価関数「mean IoU」を使用します。
・評価値は0～1の値をとり、精度が高いほど大きな値となります。


評価関数の詳細
　①全ラベルの予測領域と正解領域（矩形）の重なり（IoU）を計算
　②各画像に対して、ラベル毎の平均IoUを計算
　③各画像に対して、画像毎の平均IoU（②の平均値）を計算
　④評価対象の全画像の平均IoU（③の平均値）を計算



最終順位の決定
チャレンジ中の評価（暫定評価）は評価用データセットの一部で評価し、チャレンジ終了後の評価（最終評価）は評価用データセットの残りの部分で評価します。
リーダーボードはチャレンジ終了後時に自動的に最終評価に切り替わり、それを元に最終順位を決定します。このため、チャレンジ中とチャレンジ終了後では順位が大きく変動する場合もあります。

システムの利用
・1日5回まで投稿が可能です。
・1参加者につき1アカウントが必要です。1人で複数アカウントを利用、1アカウントを複数人で共有することは禁止です。
・チームでの参加はできません。

情報の取り扱い
 ・本コンペティション参加者が、自身で開発したアルゴリズム及びその他本コンペティションへの参加に関連して作成した資料等を自身のSNSやブログ等で一般公開することについては、公開時に本コンペティションの参加に関連して作成したものであることを明記する場合においては、何ら制限しません。
・ただし、本コンペのフォーラム以外で公開した際には、全ての参加者が閲覧できるよう、フォーラムにその旨（リンク等含む）明記してください。

データの利用
 ・第三者の権利を侵害しない、無償で誰でも手に入るオープンなデータに限り、提供データ以外のデータや、学習済モデル、API等も利用可能です。
・例えば、学習データの修正（手動でのラベル付けやラベルの書き換え等含む）も可能です。

Disclosure policy

As a general rule, in accordance with Article 4, Paragraph 1 of the terms of participation, diclosing any contents such as insights and deliverables transmitted through the information or data provided by our company in relation to this competition is not permitted, however, only after the completion of this competition and for non-commercial purposes, it will be possible to disclose the contents within the score of the table below
Model *1
Public
Analysis results *2
Public
Public : Posting to social media sites, blogs and source repositories, and citing to papers
Restricted : Using in a limited range from research, education to seminars, where many unspecified people cannot access
*1 Execution unit source code and learned models
*2 The insights obtained using the information and data provided, or the solutions including scripts and processed data such as summary statistics

※Notes

・SOTA challenge は参加者毎に終了日が異なるため、参加規約に同意いただいた後であれば公開可能です。
・本コンペのフォーラム以外で公開する際は、本コンペの参加に関連して作成したものであることを明記してください。
・本コンペのフォーラム以外で公開した際は、全ての参加者が閲覧できるよう、フォーラムにその旨（リンク等含む）明記してください。

Q：文字ラインについて、1文の中で分割している場合と分割していない場合の基準は何か？

A：レイアウトラベルごとに異なり、基準は以下のとおりです。
 - textline, handwritten, typography : 1文であっても概ねその行のフォント2文字分より離れていたら分ける
 - headline, caption : 1文であれば離れていても分けない

Q：分数を含む数式の扱いは？

A：分数かどうかを問わず、「1行の式は文字ライン1行」としています。

Q：学習用データセットには9_tableが含まれているが、評価用データでは評価しないのか？

A：評価しません。tableを学習することで予測結果の改善には利用できる可能性(illustrationの誤検出を減らす等)も考慮し、学習用データセットには残しています。

Q：文中の「……」や「―」で文字ラインの矩形が途切れるのはなぜか？

A：目次等の区切り記号に使われているためです。一括で区切り記号としています。

▼本コンペティションにおける特別規約
・本コンペティションで配布するデータは、コンペティション終了後に削除いただく必要はありません。ただし、本コンペティションへの参加を目的とする場合に限り利用できるものとします。
・本コンペティション参加者が、自身で開発したアルゴリズム及びその他本コンペティションへの参加に関連して作成した資料等を自身のSNSやブログ等で一般公開することについては、公開時に本コンペティションの参加に関連して作成したものであることを明記する場合においては、何ら制限しません。ただし、本コンペのフォーラム以外で公開した際には、全ての参加者が閲覧できるよう、フォーラムにその旨（リンク等含む）明記してください。
・本特別規約と、本コンペティション参加規約とに競合があった場合は、本特別規約が優先されます。


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Enforced on April 1, 2018
Last updated on January 18, 2019