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表紙：光学式文字認識の世界市場-2023年～2030年
市場調査レポート
商品コード
1347990

光学式文字認識の世界市場-2023年～2030年

Global Optical Character Recognition Market - 2023-2030
出版日: | 発行: DataM Intelligence | ページ情報: 英文 185 Pages | 納期: 約2営業日

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光学式文字認識の世界市場-2023年～2030年
出版日: 2023年09月11日
発行: DataM Intelligence
ページ情報: 英文 185 Pages
納期: 約2営業日
ご注意事項 :
本レポートは最新情報反映のため適宜更新し、内容構成変更を行う場合があります。ご検討の際はお問い合わせください。
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概要

概要

世界の光学式文字認識（OCR）市場は、2022年に122億米ドルに達し、2023年から2030年の予測期間中にCAGR 15.2%で成長し、2030年には316億米ドルに達すると予測されています。

さまざまな産業でデジタル変革の採用が進み、デジタル化して処理する必要のある紙文書の量が大幅に増加しています。OCRは、抽出プロセスを自動化することでデータの効率性と生産性を大幅に向上させ、手作業によるデータ入力の必要性を排除することで、エラーの削減と時間の節約につながります。

多くの企業がOCRを採用し、請求書処理、契約書管理、様々なフォームからのデータ抽出など、様々なプロセスを自動化しています。医療業界では、患者記録、カルテ、処方箋をデジタル形式に変換するOCRが広く採用されています。

北米は、世界の光学式文字認識市場の1/3以上を占める成長地域のひとつであり、この地域は技術革新の中心地です。組織は、紙ベースの文書をデジタル形式に変換するOCRを採用しています。基本的に、OCRは研究機関で使用され、アクセシビリティを高め、オンライン学習プラットフォームをサポートします。

ダイナミクス

視覚障害者のためのOCRの採用

光学式文字認識には、AI、機械学習、コンピューター・ビジョンの著しい進歩が関与しており、これらの進歩により、より正確で信頼性の高いOCR機能が実現し、印刷されたテキストを支援機器で読み取り可能なデジタル形式に正確に変換することが可能になりました。OCRをAIや機械学習技術と統合することで、認識精度を継続的に向上させることができます。

機械学習アルゴリズムは、異なるフォント、スタイル、言語に適応し、テキストを効果的に認識・変換するOCRの能力を高めることができます。例えば、2023年7月7日、インドのバンガロールにあるラマイア工科大学のIEEE Computational Intelligence Society支部の学生チームが、視覚障害者を支援するためのOurVisionと呼ばれる支援機器を開発しました。

OurVisionは、光学式文字認識（OCR）や機械学習などのコンピューター・ビジョン技術を活用し、テキストを音声で読み上げたり、ユーザーの周囲の移動を支援したりするウェアラブル・デバイスです。このプロジェクトは、IEEE財団と寛大な寄付者のパートナーシップであるEPICS in IEEEから4,400米ドルの助成を受けました。

教育分野における光学式文字認識の採用

教育機関では、生徒の記録、管理文書、評価資料など、大量の書類を扱うことが多いです。OCRは、紙ベースのフォームから情報を自動的に抽出することでデータ入力を高速化し、手作業によるデータ入力ミスを減らし、時間を節約します。教育機関の図書館や公文書館では、OCRを使用して歴史的文書、原稿、研究論文をデジタル化し、索引を付けることで、研究者や学者が簡単にアクセスできるようにしながら、貴重な情報の保存を保証しています。

例えば、2023年8月24日、世界最大のITインフラ・サービス・プロバイダーであるKyndryl社と、急成長中のオンライン高等教育サービス・プロバイダーであるUSDC Projects India Pvt Ltd社は、最先端の大学管理プラットフォームを開発・管理するための戦略的協力関係を締結しました。Kyndrylのソリューションは、AIベースの試験評価と採点、デジタル化のための光学式文字認識、高度な出席システムなどの機能を組み込み、大学特有のニーズに対応するように設計されています。

技術進歩

ディープラーニング技術、特に畳み込みニューラルネットワークとリカレントニューラルネットワークの統合により、光学式文字認識の精度が大幅に向上しました。これらのネットワークにより、OCRシステムは自動的に学習し、画像から複雑な特徴を抽出できるようになり、認識率の向上につながりました。NLP技術は、文脈と意味の理解を強化するために光学式文字認識システムに組み込まれており、これにより光学式文字認識は複雑な文書から意味のある情報を正確に解釈し、抽出することができます。

例えば、2022年12月26日、リーガル・テクノロジー・プロバイダーであるInfoTrackは、Amazon Web ServicesとChatGPTの先進技術を活用し、運送業者の完了後のプロセスを強化しています。その目的は、AP1提出を迅速化し、より高い精度を確保することです。

InfoTrackは、Amazon Web Servicesの光学式文字認識技術を利用しており、このOCR技術は、アップロードされた書類を読み取り、申請者、所有者、個人代表者、住宅ローンの詳細などのデータを抽出します。その後、ChatGPTのソフトウェアがAP1フォームの自動作成に使用され、InfoTrackのシステム内で検証されます。

市場需要に影響する品質の低さ

OCRの精度は、入力画像の品質に大きく依存します。低解像度、ぼやけ、歪み、ノイズなどの要因による画質の悪さは、文字認識のエラーにつながります。OCRアルゴリズムは、複雑なフォント、手書きテキスト、スタイル化された文字の認識に苦戦することがあります。手書きのバリエーションや芸術的なフォントは、不正確な結果を招く可能性があります。

OCRでは、文書の元の書式やレイアウトを保持することが困難な場合があり、その結果、段組、表、ヘッダー、フッター、その他の構造要素の維持にエラーが発生することがあります。OCRシステムは、処理されるドキュメントのタイプによって動作が異なる場合があります。レイアウトのバリエーション、フォントの変更、文書固有の書式設定は、認識精度に影響を与える可能性があります。

目次

第1章 調査手法と調査範囲

第2章 定義と概要

第3章 エグゼクティブサマリー

第4章 市場力学

  • 影響要因
    • 促進要因
      • 視覚障害者向けOCRの採用
      • 教育分野における光学式文字認識の採用
      • 技術進歩
    • 抑制要因
      • 市場の需要に影響を与える品質の低さ
    • 機会
    • 影響分析

第5章 産業分析

  • ポーターのファイブフォース分析
  • サプライチェーン分析
  • 価格分析
  • 規制分析
  • ロシア・ウクライナ戦争の影響分析
  • DMIの見解

第6章 COVID-19分析

第7章 タイプ別

  • ソフトウェア
  • サービス

第8章 用途別

  • 小売業
  • BFSI
  • 政府機関
  • IT・通信
  • 運輸・物流
  • 医療
  • その他

第9章 エンドユーザー別

  • B2B
  • B2C

第10章 地域別

  • 北米
    • 米国
    • カナダ
    • メキシコ
  • 欧州
    • ドイツ
    • 英国
    • フランス
    • イタリア
    • ロシア
    • その他欧州
  • 南米
    • ブラジル
    • アルゼンチン
    • その他南米
  • アジア太平洋
    • 中国
    • インド
    • 日本
    • オーストラリア
    • その他アジア太平洋地域
  • 中東・アフリカ

第11章 競合情勢

  • 競合シナリオ
  • 市況/シェア分析
  • M&A分析

第12章 企業プロファイル

  • ABBYY
    • 企業概要
    • 製品ポートフォリオと説明
    • 財務概要
    • 主な動向
  • Adobe
  • Captricity Inc.
  • Anyline Gmbh
  • ATAPY Software
  • Google LLC
  • IRIS S.A
  • Microsoft
  • NAVER Crop
  • Open Text Corporation

第13章 付録

目次
Product Code: ICT6858

Overview

Global Optical Character Recognition Market reached US$ 12.2 billion in 2022 and is expected to reach US$ 31.6 billion by 2030, growing with a CAGR of 15.2% during the forecast period 2023-2030.

The ongoing changes in the adoption of digital transformation across various industries led to have massive increase in the volume of paper documents that need to be digitized and processed. OCR significantly enhances the efficiency and productivity of data by automating the extraction process and it eliminates the need for manual data entry which leads to reduced errors and saves time.

Many businesses are adopting OCR that automates various processes such as invoice processing, contract management and data extraction from various forms and this automation leads to have faster decision-making process and improved efficiency. The healthcare industry has wider adoption of OCR as it converts patient records, medical charts and prescriptions into digital format.

North America is among the growing regions in the global optical character recognition market covering more than 1/3rd of the market and the region is the hub for technological innovations. Organizations are adopting OCR which converts paper-based documents into digital format. Basically, OCR is used in research institutes and it enhances accessibility and supports online learning platforms.

Dynamics

Adoption of OCR for Visually Impaired Person

Optical character recognition has involved significant advancements in AI, machine learning and computer vision and these advancements have more accurate and reliable OCR capabilities which makes it feasible to accurately convert printed text into digital formats that can be read by assistive devices. The integration of OCR with AI and machine learning technologies enables continuous improvement of recognition accuracy.

Machine learning algorithms can adapt to different fonts, styles and languages, enhancing the OCR's ability to recognize and convert text effectively. For instance, on 7 July 2023, a team of students from the Ramaiah Institute of Technology's IEEE Computational Intelligence Society chapter in Bangalore, India, has developed an assistive device called OurVision to aid people who are visually impaired.

OurVision is a wearable device that utilizes computer vision techniques, including optical character recognition (OCR) and machine learning, to read text aloud and assist users in navigating their surroundings. The project received a grant of US$ 4,400 from EPICS in IEEE, a partnership between IEEE Foundation and generous donors.

Adoption of Optical Character Recognition in the Education Sector

Educational institutions often handle a large volume of paperwork, including student records, administrative documents and assessment materials. OCR speeds up data entry by automatically extracting information from paper-based forms, reducing manual data input errors and saving time. Libraries and archives in educational institutions use OCR to digitize and index historical documents, manuscripts and research papers and this ensures the preservation of valuable information while making it easily accessible to researchers and scholars.

For instance, on 24 August 2023, Kyndryl, the world's largest IT infrastructure services provider and USDC Projects India Pvt Ltd, a fast-growing online higher education services provider, have entered into a strategic collaboration to develop and manage a state-of-the-art university management platform. Kyndryl's solution is designed to cater to universities' specific needs, incorporating features such as AI-based exam evaluations and scoring, optical character recognition for digitization and an advanced attendance system.

Technology Advancement

The integration of deep learning techniques especially convolutional neural networks and recurrent neural networks, has greatly improved optical character recognition accuracy and these networks enable OCR systems to automatically learn and extract complex features from images, leading to higher recognition rates. NLP techniques have been incorporated into optical character recognition systems to enhance their understanding of context and semantics and this enables optical character recognition to accurately interpret and extract meaningful information from complex documents.

For instance, on 26 December 2022, InfoTrack, a legal technology provider, is leveraging advanced technologies from Amazon Web Services and ChatGPT to enhance the post-completion process for conveyancers. The goal is to accelerate AP1 submissions and ensure higher accuracy in the process.

InfoTrack utilizes Optical Character Recognition technology from Amazon Web Services and this OCR technology reads the uploaded documents, extracting data such as Applicants, Proprietors, Personal Representatives and Mortgage Details. Subsequently, ChatGPT's software is employed to automate the population of the AP1 form and validate it within InfoTrack's system.

Poor Quality Affecting the Market Demand

OCR accuracy is highly dependent on the quality of the input image. Poor image quality due to factors like low resolution, blurriness, distortion or noise can lead to errors in character recognition. OCR algorithms may struggle with recognizing complex fonts, handwritten text or stylized characters. Handwriting variations and artistic fonts can result in inaccuracies.

OCR may have difficulty preserving the original formatting and layout of the document and this can lead to errors in maintaining columns, tables, headers, footers and other structural elements. OCR systems may perform differently based on the type of document being processed. Layout variations, font changes and document-specific formatting can affect recognition accuracy.

Segment Analysis

The global optical character recognition market is segmented based type, application, end-user and region.

Digitalized Content and Leading Software Solutions Increases Market Demand Software is expected to be the major segment fueling the market growth with a share of about 1/3rd during the forecast period. As more content becomes digitized, there is a growing need to convert printed and handwritten documents into machine-readable text. Optical character recognition software plays a crucial role in this digital transformation process.

Optical character recognition software that supports multiple languages is in high demand as companies operate on a global scale. The ability to recognize and process text in different languages is essential for accurate data extraction and translation.

For instance, on 25 October 2022, Inspur Information, a leading IT infrastructure solutions provider, collaborated with Upstage, a Korean AI company, to build an advanced AI server architecture platform. Upstage is developing an AI-based B2B no-code/low-code software solution called AI Pack, with a core application named OCR Pack for document recognition.

Geographical Penetration

The Rising Digitalization and Partnerships in Asia-Pacific

Asia-Pacific is among the major regions in the global optical character recognition market covering around 1/4th of the market in 2022. The region actively pursuing digital transformation initiatives across various sectors, including government, finance, healthcare and education. OCR plays a crucial role in digitizing and processing large volumes of paper-based documents, contributing to overall digitalization efforts.

For instance, on 24 August 2022, Tata Power Delhi Distribution Ltd implemented an AI-based forensic meter reading solution in collaboration with data capture and AI developer Anyline. This solution employs optical character recognition technology to enhance meter reading accuracy and reduce non-technical losses for the North Delhi region. The partnership with Anyline reflects Tata Power-DDL's commitment to leveraging advanced technologies to benefit its customers.

Competitive Landscape

The major global players in the market include: ABBYY, Adobe, Captricity Inc., Anyline Gmbh, ATAPY Software, Google LLC, IRIS S.A, Microsoft, NAVER Crop and Open Text Corporation.

COVID-19 Impact Analysis

The pandemic accelerated the adoption of remote work and digital transformation across industries. As organizations shifted to remote operations, the demand for digitizing documents and automating data extraction through OCR increased. OCR played a crucial role in enabling remote workers to access and process information from scanned or printed documents.

The healthcare sector experienced an increased need for efficient data processing due to the pandemic. OCR helped healthcare professionals digitize and extract valuable information from medical records, test results and other documents, facilitating faster decision-making and patient care. Researchers and public health agencies needed to analyze a vast amount of data related to COVID-19 cases, treatments and outcomes.

AI Impact

AI-powered OCR systems use advanced machine learning algorithms to recognize characters and patterns in images and this results in higher accuracy rates compared to traditional OCR methods, especially when dealing with complex fonts, handwritten text or degraded images. AI-driven OCR solutions can support a wider range of languages and scripts. Machine learning models can be trained on diverse language datasets, enabling OCR systems to accurately recognize text in various languages.

AI-based OCR can adapt and learn from new data and this adaptability allows the system to improve its accuracy over time as it encounters more diverse examples, making it suitable for applications with evolving content. AI-powered OCR systems can analyze context and semantics to better interpret the meaning of text and this contextual understanding enables better comprehension of documents and supports more intelligent data extraction.

For instance, on 16 August 2023, Tricentis' Vision AI, an AI-based test automation feature in the company's flagship product Tricentis Tosca, the method and system for single pass OCR was invented by David Colwell. Vision AI employs a neural network comprising multiple algorithms to simultaneously scan multiple images around text and this advancement significantly improves the speed of OCR technology, reducing response time from an average of one second to just 40 milliseconds.

Russia- Ukraine War Impact

The conflict led to the closure or disruption of key transportation routes between Russia and Ukraine. Border closures, checkpoints and conflict zones have hindered the movement of goods by road, rail and even air in some cases. The conflict has disrupted supply chains that rely on the efficient movement of goods between Russia, Ukraine and neighboring countries. Companies that source raw materials, components or finished products from these regions have had to seek alternative routes or suppliers.

Trade between Russia and Ukraine, as well as with other countries, has been affected. Export and import activities have faced delays, restrictions and uncertainty due to the conflict and this has impacted industries dependent on cross-border trade. Transportation costs have risen due to the need for alternative routes, longer transit times and security measures. Uncertainty about the situation has also made long-term logistics planning more challenging.

By Type

  • Software
  • Services

By Application

  • Retail
  • BFSI
  • Government
  • IT Telecom
  • Transport and Logistics
  • Healthcare
  • Others

By End-User

  • B2B
  • B2C

By Region

  • North America
    • U.S.
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • France
    • Italy
    • Russia
    • Rest of Europe
  • South America
    • Brazil
    • Argentina
    • Rest of South America
  • Asia-Pacific
    • China
    • India
    • Japan
    • Australia
    • Rest of Asia-Pacific
  • Middle East and Africa

Key Developments

  • On 31 July 2023, Bluevine, a prominent provider of solutions for small business banking, unveiled its latest offering: an accounts payable solution aimed at simplifying the management of business payments for owners and their teams within their Bluevine Business Checking accounts. The platform employs optical character recognition to extract data from the bills, presenting them to users for quick verification and confirmation.
  • On 2 August 2023, Viaccess-Orca (VO), Multi-Development and Construction Corporation (MDCC), Jose Paolo Calma, introduced Homeqube, a blockchain and artificial intelligence (AI)-driven homebuilding platform. The platform includes optical character recognition for automatic lot area plotting, agile design capabilities and auto-generation of essential documents before move-in.
  • On 11 July 2023, Smart Data Solutions expanded its operations into India by establishing a Center of Excellence in Chennai and its expansion reflects the company's commitment for enhancing its capabilities and services in the region. The Center of Excellence is equipped with advanced technologies, including Optical Character Recognition to streamline data processing and deliver efficient solutions.

Why Purchase the Report?

  • To visualize the global optical character recognition market segmentation based on type, application, end-user and region, as well as understand key commercial assets and players.
  • Identify commercial opportunities by analyzing trends and co-development.
  • Excel data sheet with numerous data points of optical character recognition market-level with all segments.
  • PDF report consists of a comprehensive analysis after exhaustive qualitative interviews and an in-depth study.
  • Product mapping available as excel consisting of key products of all the major players.

The global optical character recognition market report would provide approximately 61 tables, 59 figures and 185 pages.

Target Audience 2023

  • Manufacturers/ Buyers
  • Industry Investors/Investment Bankers
  • Research Professionals
  • Emerging Companies

Table of Contents

1. Methodology and Scope

  • 1.1. Research Methodology
  • 1.2. Research Objective and Scope of the Report

2. Definition and Overview

3. Executive Summary

  • 3.1. Snippet by Type
  • 3.2. Snippet by Application
  • 3.3. Snippet by End-User
  • 3.4. Snippet by Region

4. Dynamics

  • 4.1. Impacting Factors
    • 4.1.1. Drivers
      • 4.1.1.1. Adoption of OCR for Visually Impaired Person
      • 4.1.1.2. Adoption of Optical Character Recognition in the Education Sector
      • 4.1.1.3. Technology Advancement
    • 4.1.2. Restraints
      • 4.1.2.1. Poor Quality Affecting the Demand of Market
    • 4.1.3. Opportunity
    • 4.1.4. Impact Analysis

5. Industry Analysis

  • 5.1. Porter's Five Force Analysis
  • 5.2. Supply Chain Analysis
  • 5.3. Pricing Analysis
  • 5.4. Regulatory Analysis
  • 5.5. Russia-Ukraine War Impact Analysis
  • 5.6. DMI Opinion

6. COVID-19 Analysis

  • 6.1. Analysis of COVID-19
    • 6.1.1. Scenario Before COVID
    • 6.1.2. Scenario During COVID
    • 6.1.3. Scenario Post COVID
  • 6.2. Pricing Dynamics Amid COVID-19
  • 6.3. Demand-Supply Spectrum
  • 6.4. Government Initiatives Related to the Market During Pandemic
  • 6.5. Manufacturers Strategic Initiatives
  • 6.6. Conclusion

7. By Type

  • 7.1. Introduction
    • 7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Type
    • 7.1.2. Market Attractiveness Index, By Type
  • 7.2. Software*
    • 7.2.1. Introduction
    • 7.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 7.3. Services

8. By Application

  • 8.1. Introduction
    • 8.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 8.1.2. Market Attractiveness Index, By Application
  • 8.2. Retail*
    • 8.2.1. Introduction
    • 8.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 8.3. BFSI
  • 8.4. Government
  • 8.5. IT Telecom
  • 8.6. Transport and Logistics
  • 8.7. Healthcare
  • 8.8. Others

9. By End-User

  • 9.1. Introduction
    • 9.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 9.1.2. Market Attractiveness Index, By End-User
  • 9.2. B2B*
    • 9.2.1. Introduction
    • 9.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 9.3. B2C

10. By Region

  • 10.1. Introduction
    • 10.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Region
    • 10.1.2. Market Attractiveness Index, By Region
  • 10.2. North America
    • 10.2.1. Introduction
    • 10.2.2. Key Region-Specific Dynamics
    • 10.2.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Type
    • 10.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 10.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 10.2.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 10.2.6.1. U.S.
      • 10.2.6.2. Canada
      • 10.2.6.3. Mexico
  • 10.3. Europe
    • 10.3.1. Introduction
    • 10.3.2. Key Region-Specific Dynamics
    • 10.3.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Type
    • 10.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 10.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 10.3.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 10.3.6.1. Germany
      • 10.3.6.2. UK
      • 10.3.6.3. France
      • 10.3.6.4. Italy
      • 10.3.6.5. Russia
      • 10.3.6.6. Rest of Europe
  • 10.4. South America
    • 10.4.1. Introduction
    • 10.4.2. Key Region-Specific Dynamics
    • 10.4.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Type
    • 10.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 10.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 10.4.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 10.4.6.1. Brazil
      • 10.4.6.2. Argentina
      • 10.4.6.3. Rest of South America
  • 10.5. Asia-Pacific
    • 10.5.1. Introduction
    • 10.5.2. Key Region-Specific Dynamics
    • 10.5.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Type
    • 10.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 10.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 10.5.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 10.5.6.1. China
      • 10.5.6.2. India
      • 10.5.6.3. Japan
      • 10.5.6.4. Australia
      • 10.5.6.5. Rest of Asia-Pacific
  • 10.6. Middle East and Africa
    • 10.6.1. Introduction
    • 10.6.2. Key Region-Specific Dynamics
    • 10.6.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Type
    • 10.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 10.6.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User

11. Competitive Landscape

  • 11.1. Competitive Scenario
  • 11.2. Market Positioning/Share Analysis
  • 11.3. Mergers and Acquisitions Analysis

12. Company Profiles

  • 12.1. ABBYY*
    • 12.1.1. Company Overview
    • 12.1.2. Product Portfolio and Description
    • 12.1.3. Financial Overview
    • 12.1.4. Key Developments
  • 12.2. Adobe
  • 12.3. Captricity Inc.
  • 12.4. Anyline Gmbh
  • 12.5. ATAPY Software
  • 12.6. Google LLC
  • 12.7. IRIS S.A
  • 12.8. Microsoft
  • 12.9. NAVER Crop
  • 12.10. Open Text Corporation

LIST NOT EXHAUSTIVE

13. Appendix

  • 13.1. About Us and Services
  • 13.2. Contact Us