Dataset

This dataset was created by Orlem

Contents

It contains the following files:

Post-OCR correction is a large corpus of 1 billion words containing original texts with a varying number of OCR mistakes and an experimental multilingual post-OCR correction output created by Pleias. Generation of Post-OCR correction was performed using HPC resources from GENCI–IDRIS (Grant 2023-AD011014736) on Jean-Zay.

  Description

All the texts come from collections integrated into Common Corpus, the largest open corpus for pretraining previously released by Pleias on HuggingFace.… See the full description on the dataset page: https://huggingface.co/datasets/PleIAs/Post-OCR-Correction.

The Optical Character Recognition (OCR) software market is experiencing robust growth, driven by the increasing digitization of documents and the need for efficient data extraction across various industries. The market, estimated at $8 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $25 billion by 2033. This growth is fueled by several key factors. The rising adoption of cloud-based OCR solutions offers scalability and cost-effectiveness, attracting businesses of all sizes. Furthermore, advancements in AI and machine learning are significantly improving OCR accuracy, particularly in handling complex document layouts and diverse handwriting styles. Increased automation needs in sectors like healthcare, finance, and government, where large volumes of paper-based records require processing, are further bolstering market expansion. The segmentation reveals a strong demand across all enterprise sizes, with large enterprises leading the adoption due to their substantial data processing requirements. However, the small and medium-sized enterprise (SME) segment is also witnessing significant growth, driven by the availability of affordable and user-friendly OCR solutions. Geographic distribution shows a strong concentration in North America and Europe, but rapidly expanding markets in Asia-Pacific, particularly in China and India, are poised to contribute significantly to future growth. Pricing tiers are strategically positioned to cater to the diverse needs and budgets of different users. The competitive landscape is dynamic, with established players like ABBYY and Adobe competing alongside innovative startups offering specialized OCR solutions. The market's future trajectory hinges on continuous innovation in AI-powered OCR technologies, the expansion of cloud-based offerings, and the growing adoption of OCR in emerging markets. Addressing challenges such as data security and privacy concerns, as well as ensuring accuracy in handling diverse document formats and languages, remain crucial for sustainable market growth. Strategic partnerships and acquisitions are likely to shape the competitive landscape in the coming years, with a focus on enhancing product capabilities and expanding market reach. The long-term outlook for the OCR software market remains highly positive, underpinned by the ongoing digital transformation and the ever-increasing need for efficient document processing across industries.

Ocr Character Current 29 Big Characters

## Overview

Ocr Character Current 29 Big Characters is a dataset for object detection tasks - it contains  Hpp7 H2H0 Tt0M annotations for 7,179 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The global Optical Character Recognition (OCR) Scanning Services market is experiencing robust growth, projected to reach a value of $984 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 13.6% from 2025 to 2033. This expansion is driven by several key factors. The increasing digitization of businesses across various sectors, coupled with the need for efficient data management and automation, fuels demand for accurate and rapid OCR solutions. Large enterprises and SMEs alike are adopting cloud-based OCR services to streamline their workflows, reduce manual data entry errors, and improve overall productivity. Furthermore, advancements in Artificial Intelligence (AI) and Machine Learning (ML) are enhancing OCR accuracy and capabilities, particularly in handling complex document formats and handwritten text. The market is segmented by application (Large Enterprise, SMEs) and type (On-premise, Cloud-Based), with the cloud-based segment witnessing faster growth due to its scalability, cost-effectiveness, and accessibility. Geographic expansion, particularly in rapidly developing economies in Asia-Pacific and regions with increasing digital literacy, contributes significantly to market growth. However, concerns regarding data security and privacy, along with the initial investment costs associated with implementing OCR systems, might pose challenges to market expansion. The competitive landscape of the OCR Scanning Services market is characterized by a mix of established players and emerging technology providers. Companies like ABBYY, Adobe, and Wondershare offer comprehensive OCR solutions integrated into their broader software portfolios. Specialized OCR providers like Veryfi and Hyland Software cater to niche market segments with tailored solutions. The market's continued growth will likely lead to increased competition, innovation in solution offerings, and strategic partnerships to expand market reach and address evolving customer needs. The historical period (2019-2024) likely showed a similar growth trajectory, laying a strong foundation for the forecasted expansion. This growth trajectory is anticipated to continue based on the ongoing trends of digital transformation and the increasing reliance on automated data processing across industries.

Online OCR Software Market Outlook

The global market size for online OCR software was valued at USD 8.5 billion in 2023 and is projected to reach USD 18.2 billion by 2032, growing at a CAGR of 8.5% during the forecast period. This impressive growth is driven by multiple factors including advancements in artificial intelligence and machine learning, the increasing digitization of business processes, and the expanding adoption of OCR technology across various industries. As organizations continue to seek efficient ways to manage and process vast amounts of data, the demand for OCR software is anticipated to surge.

One of the major growth factors for the online OCR software market is the need for enhanced data accuracy and efficiency in business operations. OCR technology significantly reduces the time and effort required to convert various formats of documents into editable and searchable data. This has a profound impact on industries such as BFSI, healthcare, and retail, where large volumes of documentation are handled daily. The push towards automation and cost-saving initiatives further propels the adoption of OCR software, particularly in large enterprises looking to streamline their workflows.

Another pivotal growth driver is the increasing penetration of cloud-based solutions. Cloud deployment offers several benefits, including scalability, cost-effectiveness, and ease of access from remote locations. As businesses continue to embrace cloud technology, the demand for cloud-based OCR software is expected to rise. Additionally, the ongoing advancements in AI and machine learning algorithms are enhancing the capabilities of OCR software, making it more accurate and efficient in recognizing and processing texts from diverse document types.

Emerging markets, especially in the Asia Pacific region, are also contributing significantly to the growth of the OCR software market. These regions are experiencing rapid digital transformation and industrialization, leading to a higher adoption of advanced technologies. Government initiatives promoting digital literacy and document digitization are further driving the market. Moreover, the increasing number of small and medium enterprises in these regions is creating a substantial demand for OCR solutions that can help them manage their data more efficiently.

From a regional perspective, North America held the largest market share in 2023, driven by the early adoption of advanced technologies and the presence of major market players in the region. Europe followed closely, with significant contributions from countries like Germany, the UK, and France. The Asia Pacific region, however, is expected to exhibit the highest growth rate during the forecast period, supported by technological advancements and increasing investments in digital infrastructure. Latin America, and the Middle East & Africa, although smaller in market size, are also projected to experience steady growth due to ongoing digitalization efforts.

Component Analysis

The online OCR software market is segmented by components into software and services. The software segment encompasses various OCR solutions that can be integrated into business processes to ensure efficient document handling and data extraction. This segment is projected to hold the largest market share due to the widespread need for automated data entry and document management solutions. These software solutions are increasingly incorporating advanced AI and machine learning algorithms, enhancing their accuracy and efficiency in recognizing complex characters and layouts. Additionally, the growing use of mobile devices for document scanning is further propelling the demand for OCR software.

On the other hand, the services segment includes professional services, such as implementation, maintenance, consulting, and training services. As organizations adopt OCR software, they often require specialized services to integrate these solutions into their existing infrastructure seamlessly. The services segment is expected to witness substantial growth, driven by the need for ongoing support and customization of OCR solutions to meet specific business requirements. Furthermore, as OCR technology evolves, businesses will require updated training and consulting services to leverage the full potential of these innovations. The increasing complexity of OCR solutions also mandates regular maintenance and updates, contributing to the growth of this segment.&

Bengali OCR Dataset 1M is a comprehensive dataset designed for training and evaluating Optical Character Recognition (OCR) models for the Bengali language. It consists of 1 million high-quality annotated images containing printed and handwritten Bengali text, covering diverse fonts, styles, and writing conditions. This dataset aims to enhance OCR research and development for Bengali script, enabling advancements in document digitization, text recognition, and AI-driven language processing applications.

The global online Optical Character Recognition (OCR) software market is experiencing robust growth, driven by the increasing digitization of businesses and the need for efficient document processing. The market, estimated at $5 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $15 billion by 2033. This growth is fueled by several key factors. The rising adoption of cloud-based solutions offers scalability and cost-effectiveness, attracting both Small and Medium-sized Enterprises (SMEs) and large enterprises. Furthermore, advancements in AI and machine learning are enhancing OCR accuracy and speed, processing complex document layouts and various languages with greater efficiency. The increasing demand for automation in various industries, including finance, healthcare, and legal, is further driving market expansion. While data security and privacy concerns pose some restraints, the overall market outlook remains highly positive due to the continuous innovation in OCR technology and its widespread applicability across diverse sectors. The market segmentation reveals significant opportunities across different user types and platforms. Mobile device usage is contributing to market growth, reflecting the need for on-the-go document processing. Large enterprises dominate market share due to their higher investment capacity in advanced OCR solutions. However, the SME segment shows significant growth potential, as businesses of all sizes recognize the benefits of automated document workflows. Geographically, North America and Europe currently hold a larger market share, but the Asia-Pacific region is emerging as a significant growth area due to rapid technological adoption and economic expansion in countries like China and India. Key players like ABBYY, Google Cloud Vision API, Amazon Textract, and others are constantly innovating to improve their offerings, leading to a competitive yet dynamic market landscape. This competition fosters innovation, resulting in increasingly sophisticated and user-friendly online OCR software solutions.

The AI OCR software market, valued at $932 million in 2025, is experiencing robust growth, projected to expand at a compound annual growth rate (CAGR) of 12.7% from 2025 to 2033. This surge is fueled by several key drivers. The increasing digitization of businesses across various sectors necessitates efficient document processing solutions, making AI-powered OCR a critical tool for automation. Furthermore, advancements in deep learning and neural networks are continuously enhancing the accuracy and speed of OCR, leading to wider adoption. The growing need for improved data extraction and analysis, particularly in finance, healthcare, and legal sectors, further contributes to market expansion. The market is segmented by application (large enterprises and SMEs) and deployment type (on-premise and cloud-based), with cloud-based solutions gaining significant traction due to their scalability and cost-effectiveness. Competition is fierce, with established players like Adobe, Google, and Microsoft alongside specialized AI OCR providers such as Wondershare, Veryfi, and ABBYY vying for market share. The market's geographic spread is also noteworthy, with North America and Europe currently dominating, while Asia-Pacific is expected to witness significant growth in the coming years driven by increasing digitalization and technological advancements in regions like China and India. The restraints to growth primarily involve data security concerns, integration complexities, and the high initial investment required for implementation, particularly for smaller businesses. However, the continuous innovation in the field, particularly the development of more accurate and robust solutions, is expected to mitigate these limitations. The forecast period of 2025-2033 shows significant potential for continued market expansion. The 12.7% CAGR suggests a substantial increase in market value, driven by factors such as the growing adoption of cloud-based solutions, increasing demand for automated document processing in various industries, and continuous improvement in AI OCR technology. The competitive landscape, while intense, fosters innovation and drives down costs, further stimulating market growth. Regional variations in adoption rates are expected, with regions experiencing faster digital transformation likely to demonstrate higher growth rates. Strategic partnerships between AI OCR vendors and enterprise software providers are also anticipated to play a crucial role in expanding market penetration and fostering wider adoption across diverse industries. The future of the AI OCR software market looks bright, promising increased efficiency, reduced operational costs, and enhanced data management capabilities for businesses worldwide.

The OCR scanning services market, valued at $984 million in 2025, is experiencing robust growth, projected to expand at a 13.6% CAGR from 2025 to 2033. This growth is fueled by several key factors. The increasing digitization of businesses across all sectors, coupled with the rising need for efficient document processing and data management, is driving strong demand for OCR solutions. Large enterprises are adopting these services for automating workflows, improving data accuracy, and reducing operational costs. Simultaneously, SMEs are increasingly adopting cloud-based OCR solutions due to their affordability and scalability, contributing significantly to market expansion. Furthermore, advancements in AI and machine learning are enhancing OCR accuracy and capabilities, broadening the applications across diverse industries, including healthcare, finance, and legal. The market is segmented by application (large enterprises and SMEs) and deployment type (on-premise and cloud-based), with the cloud-based segment witnessing faster growth due to its inherent flexibility and cost-effectiveness. Geographic expansion is also a significant driver, with North America and Europe currently dominating the market, but the Asia-Pacific region showcasing significant growth potential due to rapid technological adoption and economic expansion. The competitive landscape is characterized by a mix of established players and emerging technology providers. Established players like ABBYY, Adobe, and Hyland Software leverage their strong brand reputation and extensive product portfolios to maintain market leadership. However, innovative startups and specialized OCR providers are gaining traction by offering niche solutions and cutting-edge technologies. The market is expected to witness increased mergers and acquisitions as larger players seek to expand their market share and enhance their technological capabilities. While data security and privacy concerns represent a potential restraint, the industry is addressing these challenges through enhanced security protocols and compliance certifications. The future trajectory indicates sustained growth, driven by continuous innovation, expanding applications, and increasing adoption across various industries and geographical regions.

Text labels are an integral part of cadastral maps and floor plans. Text is also prevalent in natural scenes around us in the form of road signs, billboards, house numbers and place names. Extracting this text can provide additional context and details about the places the text describes and the information it conveys. Digitization of documents and extracting texts from them helps in retrieving and archiving of important information.This deep learning model is based on the MMOCR model and uses optical character recognition (OCR) technology to detect text in images. This model was trained on a large dataset of different types and styles of text with diverse background and contexts, allowing for precise text extraction. It can be applied to various tasks such as automatically detecting and reading text from documents, sign boards, scanned maps, etc., thereby converting images containing text to actionable data.Using the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Fine-tuning the modelThis model cannot be fine-tuned using ArcGIS tools.InputHigh-resolution, 3-band street-level imagery/oriented imagery, scanned maps, or documents, with medium to large size text.OutputA feature layer with the recognized text and bounding box around it.Model architectureThis model is based on the open-source MMOCR model by MMLab.Sample resultsHere are a few results from the model.

Optical Character Recognition (OCR) Systems Market Outlook

The global market size for Optical Character Recognition (OCR) systems was valued at approximately USD 8.1 billion in 2023 and is projected to reach around USD 16.5 billion by 2032, growing at a CAGR of 8.3% during the forecast period. This growth is primarily driven by the increasing demand for automated data entry and document digitization across various industries.

One of the primary growth factors for the OCR systems market is the rising demand for efficient document management solutions. Businesses and organizations are increasingly adopting OCR systems to convert different types of documents, such as scanned paper documents, PDF files, or images captured by digital cameras, into editable and searchable data. This not only improves operational efficiency but also helps in reducing manual data entry errors. Furthermore, the increasing adoption of OCR technology in government initiatives for digital transformation and smart city projects is also contributing to market growth.

Another significant growth factor is the integration of artificial intelligence (AI) and machine learning (ML) technologies with OCR systems. The incorporation of AI and ML enhances the accuracy and efficiency of OCR systems by enabling them to recognize and process a wide variety of fonts and handwriting styles. This advancement is particularly valuable in sectors such as healthcare and banking, where precision and accuracy are paramount. AI-driven OCR systems can also adapt and learn from new data inputs, making them more versatile and capable over time.

The growing use of OCR technology in the retail and e-commerce sectors is also fueling market growth. Retailers are leveraging OCR systems to streamline various processes, such as inventory management, billing, and customer relationship management. By automating these tasks, businesses can save time and costs while improving customer satisfaction. Additionally, the increasing trend of online shopping has led to a surge in the demand for OCR systems to handle large volumes of digital documents and receipts.

Regionally, North America holds a significant share of the OCR systems market, primarily due to the early adoption of advanced technologies and the presence of major market players in the region. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by the rapid digitalization efforts, growing industrialization, and increasing adoption of OCR systems in emerging economies such as China and India. Europe is also a key market for OCR systems, with a strong focus on regulatory compliance and data management solutions.

In addition to OCR systems, Image Recognition Software is gaining traction as a complementary technology that enhances document management solutions. This software is capable of identifying and categorizing images, which is particularly useful in sectors such as retail and healthcare. For instance, in retail, image recognition can be used to automate inventory management by recognizing product images and updating stock levels accordingly. In healthcare, it can assist in diagnosing medical images, thereby improving patient outcomes. The integration of image recognition with OCR systems can lead to more comprehensive data analysis and processing capabilities, offering businesses a competitive edge.

Component Analysis

The OCR systems market can be segmented by components into software, hardware, and services. Software components dominate the market, driven by the increasing demand for advanced OCR solutions that offer high accuracy and efficiency. OCR software is integral for converting different types of documents and images into editable and searchable data. The continuous advancements in AI and ML technologies are further enhancing the capabilities of OCR software, making it more reliable and versatile.

Hardware components, although not as large as the software segment, play a crucial role in the OCR systems market. This segment includes scanners, cameras, and other devices used to capture images of documents. The demand for high-resolution scanners and advanced imaging devices is growing, particularly in sectors like healthcare and banking, where precision is vital. Innovations in hardware technology, such as the development of portable and wireless scanners, are also contributing to the growth of this segment.

SARD: Synthetic Arabic Recognition Dataset

  Overview

SARD (Synthetic Arabic Recognition Dataset) is a large-scale, synthetically generated dataset designed for training and evaluating Optical Character Recognition (OCR) models for Arabic text. This dataset addresses the critical need for comprehensive Arabic text recognition resources by providing controlled, diverse, and scalable training data that simulates real-world book layouts.

  Key Features

Massive… See the full description on the dataset page: https://huggingface.co/datasets/riotu-lab/SARD.

OCR Software Market Outlook

The global OCR (Optical Character Recognition) software market size was valued at approximately USD 8.5 billion in 2023 and is projected to reach USD 15.7 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.1% during the forecast period. The expanding integration of OCR technology in various sectors, such as banking, healthcare, and education, is a primary driver of this market's growth. The increasing need for efficient document management and the conversion of physical documents into digital formats are crucial factors contributing to the rapid adoption of OCR software globally.

A significant growth factor for the OCR software market is the rising digital transformation across organizations worldwide. As businesses strive for efficiency and automation, OCR technology becomes integral in streamlining document workflows, reducing manual data entry, and minimizing errors. This technology enables organizations to convert diverse formats of documents, such as scanned paper documents, PDF files, or images captured by a digital camera, into editable and searchable data, thus significantly enhancing productivity. Additionally, the emergence of advanced technologies like AI and machine learning is boosting the capabilities of OCR systems, allowing for better accuracy and recognition rates, which further propels market demand.

The growing adoption of cloud-based solutions is another critical factor driving the OCR software market's expansion. Cloud deployment offers various advantages, including cost-efficiency, scalability, and ease of access, making it a preferred choice for businesses of all sizes. With cloud-based OCR solutions, organizations can process large volumes of documents without investing heavily in infrastructure. Moreover, cloud services provide enhanced security features and compliance with regulatory standards, which are crucial for sectors like BFSI and healthcare that deal with sensitive information. This trend towards cloud computing is anticipated to further accelerate the market's growth trajectory.

Another crucial factor contributing to market growth is the increasing application of OCR technology in the educational sector. Educational institutions are increasingly adopting OCR software to digitize textbooks, exams, and academic records, making information more accessible and manageable. This digitization facilitates smoother information retrieval, enhances learning experiences, and assists in creating interactive educational content. Furthermore, the government's initiatives to promote digital education and e-learning platforms are expected to provide additional momentum to the OCR software market during the forecast period.

Regionally, North America currently holds a significant share of the OCR software market, driven by the presence of major technology players and the rapid adoption of digital solutions across industries. The region's focus on technological advancements and innovation is expected to sustain its market dominance. Meanwhile, the Asia Pacific region is forecasted to exhibit the highest CAGR during the forecast period, attributed to the burgeoning IT sector, increasing government initiatives for digital transformation, and growing awareness of OCR benefits among businesses. Other regions like Europe and Latin America are also expected to witness steady growth due to the expanding application of OCR technologies across various sectors.

Component Analysis

In the OCR software market, the component segment is bifurcated into software and services. The software component constitutes the core of OCR solutions, encompassing various tools and applications that facilitate the conversion of images of text into machine-encoded text. The software component is witnessing substantial growth due to the increasing demand for automated document processing capabilities. As organizations aim to enhance operational efficiency, OCR software is becoming a critical asset for data handling and processing, thereby driving its adoption across multiple industries. Additionally, advancements in AI and machine learning are continuously refining OCR software, leading to enhanced accuracy and broader applicability.

Services, as a component of the OCR market, include consulting, integration, and maintenance services. These services play a pivotal role in ensuring the successful deployment and operation of OCR solutions within an organization’s infrastructure. The service segment is vital for customizing OCR software to meet specific organizational needs and integrating it with existing syste

What’s Included

Introducing the Bulgarian Shopping List Image Dataset - a diverse and comprehensive collection of handwritten text images carefully curated to propel the advancement of text recognition and optical character recognition (OCR) models designed specifically for the Bulgarian language.

Containing more than 2000 images, this Bulgarian OCR dataset offers a wide distribution of different types of shopping list images. Within this dataset, you'll discover a variety of handwritten text, including sentences, and individual item name words, quantity, comments, etc on shopping lists. The images in this dataset showcase distinct handwriting styles, fonts, font sizes, and writing variations.

To ensure diversity and robustness in training your OCR model, we allow limited (less than three) unique images in a single handwriting. This ensures we have diverse types of handwriting to train your OCR model on. Stringent measures have been taken to exclude any personally identifiable information (PII) and to ensure that in each image a minimum of 80% of space contains visible Bulgarian text.

The images have been captured under varying lighting conditions, including day and night, as well as different capture angles and backgrounds. This diversity helps build a balanced OCR dataset, featuring images in both portrait and landscape modes.

All these shopping lists were written and images were captured by native Bulgarian people to ensure text quality, prevent toxic content, and exclude PII text. We utilized the latest iOS and Android mobile devices with cameras above 5MP to maintain image quality. Images in this training dataset are available in both JPEG and HEIC formats.

In addition to the image data, you will receive structured metadata in CSV format. For each image, this metadata includes information on image orientation, country, language, and device details. Each image is correctly named to correspond with the metadata.

This metadata serves as a valuable resource for understanding and characterizing the data, aiding informed decision-making in the development of Bulgarian text recognition models.

We are committed to continually expanding this dataset by adding more images with the help of our native Bulgarian crowd community.

If you require a customized OCR dataset containing shopping list images tailored to your specific guidelines or device distribution, please don't hesitate to contact us. We have the capability to curate specialized data to meet your unique requirements.

Additionally, we can annotate or label the images with bounding boxes or transcribe the text in the images to align with your project's specific needs using our crowd community.

This image dataset, created by FutureBeeAI, is now available for commercial use.

Leverage this shopping list image OCR dataset to enhance the training and performance of text recognition, text detection, and optical character recognition models for the Bulgarian language. Your journey to improved language understanding and processing begins here.

The AI Optical Character Recognition (OCR) software market is experiencing robust growth, projected to reach a market size of $932 million in 2025, expanding at a Compound Annual Growth Rate (CAGR) of 12.7%. This expansion is fueled by several key factors. The increasing digitalization across industries necessitates efficient document processing solutions, driving demand for AI-powered OCR. Businesses, particularly large enterprises and SMEs, are adopting AI OCR to automate tasks like data entry, invoice processing, and contract management, improving operational efficiency and reducing manual labor costs. The shift towards cloud-based solutions offers scalability and accessibility, further fueling market growth. Furthermore, advancements in AI algorithms are enhancing accuracy and speed, enabling the processing of complex document formats, including handwritten text and images. Competitive landscape is characterized by a mix of established players like Adobe and Google, alongside specialized AI OCR vendors such as Wondershare and ABBYY, fostering innovation and competitive pricing. The market segmentation showcases a strong preference for cloud-based solutions, reflecting the current industry trend toward flexible and accessible software deployments. North America currently holds a significant market share, driven by early adoption and technological advancements; however, Asia-Pacific is poised for rapid growth due to increasing digitalization and expanding technological infrastructure. Looking ahead to 2033, the market is expected to continue its upward trajectory, driven by the ongoing integration of AI OCR into various applications and industries. Government initiatives promoting digital transformation and data management are also expected to contribute to market expansion. However, factors such as data security concerns, implementation costs, and the need for skilled personnel to manage and maintain these systems could pose some challenges to market growth. Nevertheless, the overall outlook remains positive, with continued advancements in AI technology promising enhanced accuracy, speed, and accessibility of AI OCR software, leading to widespread adoption across diverse sectors. The evolving nature of document formats and the need for multilingual support are also driving innovation and shaping the future of the AI OCR market.

Product recognition is a task that receives continuous attention by the computer vision/deep learning community mainly with the scope of providing robust solutions for automatic checkout supermarkets. One of the main challenges is the lack of images that illustrate in realistic conditions a high number of products. Here the product recognition task is perceived slightly differently compared to the automatic checkout paradigm but the challenges encountered are the same. The setting under which this dataset is captured is with the aim to help individuals with visual impairment in doing their daily grocery in order to increase their autonomy. In particular, we propose a large-scale dataset utilized to tackle the product recognition problem in a supermarket environment. The dataset is characterized by (a) large scale in terms of unique products associated with one or more photos from different viewpoints, (b) rich textual descriptions linked to different levels of annotation and, (c) images acquired both in laboratory conditions and in a realistic supermarket scenario portrayed in various clutter and lighting conditions. A direct comparison with existing datasets of this category demonstrates the significantly higher number of the available unique products, as well as the richness of its annotation enabling different recognition scenarios. Finally, the dataset is also benchmarked using various approaches based both on visual and textual descriptors.

OCR Scanning Software Market Outlook

The global OCR Scanning Software market size is projected to reach USD 15.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 11.2% from 2023's market value of USD 6.1 billion. This growth is driven by the increasing need for automation and digitization across various industries. The OCR (Optical Character Recognition) scanning software market is also benefiting from advancements in machine learning and artificial intelligence, which enhance the accuracy and efficiency of OCR technologies.

One of the key growth factors for this market is the increasing adoption of digital transformation strategies by organizations. Companies are investing in OCR scanning software to automate data entry tasks, thereby reducing manual errors and increasing productivity. The growing emphasis on minimizing paper usage in business operations further boosts the adoption of OCR technologies. Moreover, the integration of OCR with other advanced technologies like AI and RPA (Robotic Process Automation) is opening new avenues for market expansion.

The healthcare sector is another significant driver for the OCR scanning software market. The need to digitize patient records, prescriptions, and other medical documents is prompting healthcare providers to adopt OCR technologies. This not only helps in maintaining accurate patient records but also facilitates easy retrieval and sharing of information across different healthcare systems. Additionally, regulatory requirements for maintaining electronic health records (EHR) are further propelling the demand for OCR scanning solutions in this sector.

In the BFSI (Banking, Financial Services, and Insurance) sector, the demand for OCR scanning software is growing rapidly. Financial institutions are leveraging OCR technologies to automate the processing of checks, invoices, and other financial documents. This not only speeds up transaction times but also enhances the accuracy of data processing, thereby reducing operational risks. The increasing focus on improving customer experience and operational efficiency is driving the adoption of OCR scanning software in this sector.

Regionally, North America holds the largest share in the OCR scanning software market due to the high adoption rate of advanced technologies and digital transformation initiatives. The presence of major market players and the growing demand for OCR solutions in sectors like healthcare, BFSI, and retail further contribute to the market's growth in this region. However, Asia Pacific is expected to witness the highest growth rate during the forecast period, driven by the increasing digitization efforts and the rising number of small and medium enterprises in countries like China and India.

Component Analysis

When analyzing the OCR scanning software market by component, it is essential to understand the contributions of both software and services. The software segment, which includes licensed software, cloud-based applications, and mobile apps, constitutes the bulk of the market. The ever-evolving nature of OCR software, incorporating AI and machine learning algorithms, brings improved accuracy and versatility to the table. These advancements make OCR software indispensable across various industries for tasks like document scanning, data extraction, and automated workflow management. The continuous need for updates, customization, and integration with other enterprise systems further fuels the demand for OCR software.

The services segment, which includes professional services like consulting, implementation, training, and support, also plays a crucial role in the OCR scanning software market. As businesses increasingly adopt OCR technologies, the demand for expert guidance in deploying and optimizing these solutions grows. Professional services ensure that OCR systems are tailored to meet specific organizational needs, thereby maximizing their utility and ROI. Additionally, training and support services help organizations efficiently use and maintain their OCR systems, ensuring long-term success and customer satisfaction.

Another critical aspect of the services segment is the maintenance and upgrade services. As OCR software becomes more sophisticated, regular updates and maintenance are necessary to keep systems running smoothly and securely. These services not only address technical issues but also incorporate the latest features and improvements, keeping the OCR systems up to date with technological advancements.

The increasing complexit

Access to retrospective data is essential for understanding environmental variability and change, it is important for initializing and validating models of all kinds, and for illuminating the relationships between ecosystems and societies that depend on them. A major barrier to effective use of historical data in any discipline is the need to transform large quantities of manuscript or printed text, especially complex data tables, into formats that can be collated and analyzed by computers. However, there is currently no Optical Character Recognition (OCR) engine that can render scanned images of documents into digital text with a level of accuracy that renders human intervention unnecessary. This is especially true with respect to scientific data presented in tables or other matrix formats. The goal of this project was to build an open source citizen science mediated OCR module to facilitate transcription of complex data tables and other typescript or printed material (e.g Arctic and worldwide weather observations recorded in ship's logs), and integrate it into the Zooniverse transcription software bundle. This module will be available to the public via Zooniverse: https://www.zooniverse.org/projects/zooniverse/oldweather-ocr.

OCR Receipts from Grocery Stores Text Detection - retail dataset

The Grocery Store Receipts Dataset is a collection of photos captured from various grocery store receipts. This dataset is specifically designed for tasks related to Optical Character Recognition (OCR) and is useful for retail.

💴 For Commercial Usage: To discuss your requirements, learn about the price and buy the dataset, leave a request on TrainingData to buy the dataset

Each image in the dataset is accompanied by bounding box annotations, indicating the precise locations of specific text segments on the receipts. The text segments are categorized into four classes: item, store, date_time and total.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F4d5c600731265119bb28668959d5c357%2FFrame%2016.png?generation=1695111877176656&alt=media" alt="">

Dataset structure

• images - contains of original images of receipts
• boxes - includes bounding box labeling for the original images
• annotations.xml - contains coordinates of the bounding boxes and detected text, created for the original photo

Data Format

Each image from images folder is accompanied by an XML-annotation in the annotations.xml file indicating the coordinates of the bounding boxes and detected text . For each point, the x and y coordinates are provided.

Classes:

• store - name of the grocery store
• item - item in the receipt
• date_time - date and time of the receipt
• total - total price of the receipt

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F62643adde75dd6ca4e3f26909174ae40%2Fcarbon.png?generation=1695112527839805&alt=media" alt="">

Text Detection in the Receipts might be made in accordance with your requirements.

💴 Buy the Dataset: This is just an example of the data. Leave a request on https://trainingdata.pro/datasets to discuss your requirements, learn about the price and buy the dataset

TrainingData provides high-quality data annotation tailored to your needs

keywords: receipts reading, retail dataset, consumer goods dataset, grocery store dataset, supermarket dataset, deep learning, retail store management, pre-labeled dataset, annotations, text detection, text recognition, optical character recognition, document text recognition, detecting text-lines, object detection, scanned documents, deep-text-recognition, text area detection, text extraction, images dataset, image-to-text, object detection