AI OCR - The Difference and 3 Use Cases Compared to Traditional OCR

1. Differences from previous OCR

How has AI OCR evolved from traditional OCR? Here, we will first explain the mechanism of traditional OCR, and then look at what has become possible by utilizing AI.

1-1. What is OCR?

OCR stands for Optical Character Recognition, a technology that uses optical methods to convert text (Character) such as printed materials and handwritten characters into digital data (Recognition). After scanning printed materials with a scanner, it goes through processes such as layout analysis, line and character extraction, normalization, and feature extraction to output it as text data. In each process, it is necessary to set various features of characters and information such as corpora in advance.

 

For example, while the characters "Japan" and "Megi" can easily be distinguished by the human eye, machines may not be able to differentiate them if the printing conditions, font size, resolution, and other factors are not properly set. In such cases, it is necessary to pre-set the characteristics of the characters "日" (sun), "目" (eye), "本" (book), and "木" (tree), as well as the frequency of appearance of "Japan" and "Megi" (which is the name of a plant). If these settings are not appropriate, the accuracy of character recognition will not improve. These settings must be properly configured by humans, but the sheer amount of information to process has been a barrier to improving reading accuracy.

1-2. What is the difference between "AI OCR" and "OCR"?

With AI OCR, we have been able to utilize the power of AI to perform advanced image analysis, context understanding, and language processing. This allows for faster processing of tasks such as identifying character features and analyzing corpora, which previously required manual input. With this technology, we can now achieve higher accuracy in recognizing specialized documents and handwritten characters that were difficult to process with traditional OCR methods.

1-3. Can business efficiency be improved with AI OCR?

AI OCR can process large amounts of documents and paperwork quickly and accurately. It can greatly reduce the time and effort required for manual work, and achieve business efficiency. It can also be used for automating data management and analysis, contributing to quick decision-making and strategic planning for companies.

2. Has AI OCR spread during the COVID-19 pandemic?

The pandemic of the novel coronavirus has rapidly increased the demand for remote work and digitalization. Among them, AI OCR has become an important tool to meet the document processing needs of companies and individuals. In a situation where remote work and document sharing are increasingly necessary, AI OCR is expanding its role as an important support tool.

3. Types of AI OCR

There are various types of AI OCR, such as those specialized in specific languages, industries, or purposes, as well as different platforms such as cloud-based or on-premises. Below are some representative types of AI OCR and their respective features.

 

• General AI OCR: Used for general text recognition with multiple functions. It supports different languages and fonts, and can recognize characters with high accuracy.
• Specialized AI OCR for specific fields: Developed for specialized fields such as medical, legal, and finance. It is specialized in technical terms and specific formats, providing high accuracy and domain knowledge.
• Handlight OCR: An OCR optimized for use with smartphones and tablets.
• Cloud-based OCR: OCR provided as a cloud service. It is fast and capable of processing large amounts of data, with high scalability.
• On-premises OCR: OCR that is installed and used on local servers or computers, rather than in the cloud. Suitable for environments where data security and privacy are prioritized.

4. Tips for Choosing AI OCR

When choosing from various AI OCR options, the key is whether the necessary elements for your business are met. Here are three tips to consider when making your selection.

Hint 1: Which product is suitable for handwriting and typography?

Among the options for OCR, there are those specialized in handwritten characters and those specialized in printed characters. First, consider how much you need to handle handwritten or printed characters according to your company's needs. If you mainly handle handwritten characters, it is important to choose an OCR with high accuracy in recognizing handwritten characters. On the other hand, if printed character recognition is the main focus, choosing an OCR specialized in printed characters can lead to increased efficiency in business operations.

Hint 2: Is it possible to support languages other than Japanese?

If you are expanding your business internationally or need to handle multilingual documents, be sure to check if the OCR supports other languages. Some OCRs may be limited to specific languages. By choosing an OCR that supports multiple languages according to your company's needs, you can expect improved efficiency in document processing across multiple languages.

Hint 3: Is it possible to integrate with external systems such as RPA?

When using OCR, it is important to consider integrating the data extracted by OCR with other systems and processes to further improve work efficiency. For example, by integrating OCR with RPA (Robotic Process Automation), one of the automation tools, automatic data extraction and processing becomes possible. When choosing an OCR, be sure to check if it supports integration with external systems. By selecting an OCR that allows for smooth data integration through APIs and integration functions, it can contribute to streamlining business processes and automation.

5. Benefits of Utilization

5-1. Can be used for non-standard documents (reports)

Traditional OCR has been specialized in standardized documents and printed characters, making it difficult to recognize non-standard documents (forms). The advantage of AI OCR is its ability to handle non-standard documents. AI OCR that supports non-standard documents can read forms and handwritten documents with different formats and layouts. This allows for efficient processing of various documents within a company, reducing work time and human errors.

5-2. Can be read while considering terminology and context

AI OCR can understand terminology and context, allowing for more advanced information processing. Unlike traditional OCR, which only recognizes characters, AI OCR can analyze entire sentences and extract data while considering context and meaning. This means that even documents with specialized terminology and complex sentences, such as contracts and legal documents, can have accurate information extracted. This improves the accuracy and efficiency of document processing and can also be utilized for business decision-making and analysis.

5-3. AI that improves reading accuracy

AI OCR utilizes machine learning and deep learning technologies, allowing for continuous improvement of recognition accuracy through ongoing learning. By training on large amounts of data and accumulating experience, the accuracy of reading improves over time. It can also adapt to new patterns, languages, and formatting changes, maintaining a high level of recognition accuracy. This leads to improved accuracy and reliability of OCR, as well as higher quality document processing. Additionally, regular updates and improvements are provided, allowing for the incorporation of the latest technologies and features, making OCR even more effective.

 

With the use of AI OCR, it is now possible to enjoy the benefits of processing non-standard documents, considering terminology and context, and improving reading accuracy, which was previously difficult with traditional OCR.

6. Disadvantages of AI OCR

6-1. Fully automated processes are difficult.

AI OCR is a highly advanced technology, but it can be difficult to fully automate. In particular, there may be cases where manual intervention is necessary, such as when dealing with complex documents, poor printing conditions, or handwritten characters that are difficult for even humans to recognize. In these cases, it may be necessary for humans to manually check and correct the output of AI OCR, ensuring accuracy and correcting any recognition errors.

6-2. Not fully adapted to vertical writing

The technology of AI OCR is mainly developed and optimized for horizontal writing characters. Therefore, it is generally known that the recognition accuracy of vertical writing documents tends to be lower than that of horizontal writing. Vertical writing characters have different structures and arrangements compared to horizontal writing characters, so if the AI OCR model cannot adapt to vertical writing, accurate reading may become difficult. However, the technology of AI OCR is evolving and development for vertical writing is also progressing, so we can expect an improvement in recognition accuracy in the future.

 

Despite the challenges of full automation and vertical writing, the technology of AI OCR is constantly advancing and its accuracy and capabilities will continue to improve.

7. Usage Examples

By utilizing AI OCR, you can expect to improve work efficiency. Here, we will introduce actual case studies.

7-1. Order and Delivery Business

In order to handle orders, it is necessary to receive paper documents such as purchase orders, delivery notes, and invoices from customers and perform data entry and administrative tasks. By utilizing AI OCR, these documents can be scanned and automatically read as text data. As a result, order and delivery information can be extracted quickly and accurately, streamlining the ordering process. Additionally, the functionality of AI OCR can be used to check data consistency and errors.

ITOCHU Corporation combines RPA and AI-OCR to reduce annual ordering and receiving tasks by approximately 49,000 hours.

7-2. Accounting Tasks

In accounting tasks, expenses are recorded and processed based on paper documents such as receipts and invoices. By using AI OCR, these documents can be digitized and necessary information can be automatically extracted. This improves the accuracy and efficiency of expense recording and processing as the amount and recipient information on receipts are accurately extracted. Furthermore, by integrating AI OCR with accounting systems and software, automatic data input and reflection in the ledger becomes possible, reducing workload and human errors.

BIPROGY, Launches Provision of Robota (Robota), an AI-OCR Specialized in Accounting

7-3. Data Entry for Reports and Documents

Within a company, there are various paper forms and documents that exist, and in order to utilize that information, manual data entry is necessary. By using AI OCR, forms and documents can be scanned and automatically converted into text data. Examples include digitizing recruitment documents for the HR department and medical institutions' diagnostic reports. With the accuracy and efficiency of AI OCR, it is possible to reduce work time and improve accuracy, making data search and analysis easier. Additionally, data backup and sharing become easier, leading to improved efficiency in information storage and sharing.

GoQSystem, a Tokyo-based technical writing and translation company, has launched its AI OCR service "GoQReader" for digitizing paper documents!

8. AI OCR and Generated AI

8-1. What is AI Generation?

With the rise of "generative AI" in recent years, which can generate text and images based on simple requests and keywords, led by ChatGPT, which outputs answers as if a person were answering when asked in natural language, the use of natural language has become increasingly popular.
The main difference between generative AI and existing AI is whether it can generate new data and content based on the given data. Existing AI can recognize and classify data according to its purpose, but cannot generate new data.
For example, existing AI can recognize whether a single frame of a manga is by Osamu Tezuka, but cannot create a new work by Osamu Tezuka. With generative AI, it is possible to not only create a story, but also generate characters. In this way, various fields and industries have begun to utilize generative AI to create new value based on existing data.

Case Studies
>AI vs. "God of Manga": A 6-Month Close-Up on Human-AI Interaction

8-2. Possibilities of AI OCR and Generated AI

By combining AI OCR and generative AI, it is possible to improve work efficiency and create new value. Below are some examples.

Automatic Document Generation and Editing:
Using AI OCR, you can add or summarize text to scanned documents using a generation AI. This allows for automatic generation of document summaries and supplemental information that previously required manual work and time.

Digitalization and Automatic Tagging of Documents:
By using AI OCR, documents can be digitized and the content can be automatically analyzed to assign appropriate keywords and tags. This makes it easier to search and classify documents.

Automatic Document Repair and Regeneration:
By using AI OCR to analyze old or damaged documents, it is possible to use a generation AI to guess and complete missing parts. This allows for document repair and restoration.
These are common applications when combining AI OCR and generation AI. This combination allows for more efficient document management, content generation, and information utilization.

Case Studies
＞Utilizing GPT-4, added functionality to AI-OCR "DEEP READ"
＞Industry first (※)! Updated receipt reading AI-OCR functionality through ChatGPT integration

9. Summary of AI OCR

We have looked into AI OCR in detail so far. AI OCR is used in various business areas such as order and delivery management, accounting tasks, and data digitization of forms and documents. Its benefits include improving work efficiency and accuracy, as well as facilitating data search and analysis.

By introducing AI OCR, companies can achieve improvements and efficiency in their business processes, and enhance their competitiveness. Please consider utilizing AI OCR to maximize your business results.

10. For inquiries about utilizing AI, please contact Human Science Co., Ltd.

10-1. Utilizing the Latest Data Annotation Tools

One of the annotation tools introduced by Human Science, Annofab, allows customers to check progress and provide feedback on the cloud even during project execution. By not allowing work data to be saved on local machines, we also consider security.

10-2. 48 million records of teacher data creation

"I want to introduce AI, but I don't know where to start."

"I don't know what to ask for when outsourcing."

Please consult Human Science Co., Ltd. in such cases.

At Human Science, we are involved in AI development projects in various industries such as natural language processing, medical support, automotive, IT, manufacturing, and construction. Through direct transactions with many companies including GAFAM, we have provided over 48 million high-quality training data. We can handle various annotation projects regardless of industry, from small-scale projects to large-scale projects with 150 annotators.
>>Human Science's Annotation Services

10-3. Resource Management without Using Crowdsourcing

At Human Science, we do not use crowdsourcing and instead directly contract with workers to progress projects. We carefully assess each member's practical experience and evaluations from previous projects to form a team that can perform to the best of their abilities.

10-4. Equipped with a security room within the company

At Human Science, we have a security room that meets the ISMS standards in our Shinjuku office. This allows us to provide on-site support for highly confidential projects and ensure security. We consider confidentiality to be extremely important for all projects at our company. We continuously provide security education to our staff and pay close attention to the handling of information and data, even for remote projects.