Key terms in character recognition
There are three common forms of intelligent data processing technologies that are summarized under the term “character recognition”. These tools identify and interpret specific characters such as letters or markings on a document and translate them into a digital format.
- Optical Character Recognition: Optical character recognition technology scans documents or images of text and converts this information into digital files.
- Handwriting Recognition: Handwriting recognition, also known as Handwritten Text Recognition (HTR) or Handwriting OCR, refers to the process of converting handwritten text or characters into machine-readable text. This technology is more sophisticated than OCR because handwriting varies greatly and is more difficult to recognize and interpret. HWR systems use complex algorithms and machine learning (ML) to analyze and identify the patterns in handwritten characters.
- Intelligent Character Recognition: ICR represents an advanced form of handwriting recognition that goes beyond simple character recognition. It integrates complex machine learning (ML) methods to not only recognize individual characters, but also to understand the context and structure of the entire document. ICR technology can interpret handwriting in the context of words, sentences and paragraphs, enabling it to process complex handwritten documents and forms. Unlike traditional OCR or basic HWR systems, Intelligent Character Recognition can produce more precise and accurate results.
How does intelligent character recognition (ICR) work?
The process of intelligent character recognition (ICR) opens up a wide range of possibilities in digital data processing and analysis. But how exactly does this technology work?
- Image capture: It all starts with capturing an image and the text to be recognized. This can be done with a camera, scanner or other device capable of capturing high quality images. The quality of the image capture is crucial as it forms the basis for all further steps.
- Pre-processing: The captured image often has imperfections such as noise, distortion or uneven lighting. Image enhancement, noise reduction and normalization techniques are therefore used in the pre-processing phase. The aim is to optimize the image quality and increase the accuracy of the subsequent recognition processes.
- Segmentation: In this step, the image is divided into individual characters or text blocks. This is particularly important for handwritten texts, as characters are often close together or may overlap.
- Feature extraction: For each segmented character, specific features are extracted that represent unique characteristics. These include aspects such as stroke direction, size, curvature and much more. These features are crucial to distinguish different characters from each other and improve the accuracy of recognition.
- Recognition: The extracted features are used by machine learning algorithms, such as neural networks, to classify and recognize the characters. For effective recognition, these algorithms need to be trained on large datasets of handwritten texts to learn the patterns and variations of different handwriting.
- Dictionary and context analysis: ICR systems often use dictionaries and context analysis to improve recognition accuracy. This involves comparing recognized characters with a dictionary to identify possible words. Analyzing the context of adjacent characters can also help to correct errors, as certain combinations of characters are more likely in a language.
- Post-processing: After the characters have been recognized, errors may occur in the output due to the complexity of the handwriting. Post-processing techniques such as error correction algorithms and language modeling can be applied to refine the recognized text.
- Output generation: The end product of the ICR system is the converted digital text. This text can now be further processed. From the automation of bureaucratic processes to the analysis of historical documents – the possibilities are almost limitless.
Areas of application for intelligent character recognition (ICR)
Intelligent character recognition (ICR) is used in a wide range of industries and is revolutionizing the way handwritten and printed documents are handled. A few examples:
Transportation and logistics: fast and accurate processing of freight documents such as bills of lading and delivery bills is critical. ICR makes it possible to efficiently digitize and process these documents, speeding up delivery processes and improving the traceability of shipments. This leads to increased transparency and efficiency throughout the supply chain.
Manufacturing industry: In the manufacturing industry, work orders, quality control documents and maintenance logs are often kept by hand. By using ICR, this information can be quickly converted into digital data, resulting in more efficient data processing and improved quality assurance. This enables more precise monitoring of production processes and contributes to the optimization of manufacturing processes.
Insurance industry: In the insurance industry, the fast and accurate processing of application forms, claims reports and customer correspondence plays a crucial role. Here, the use of ICR technologies, combined with AI, offers enormous advantages for more efficient workflow automation. ICR can be used to efficiently digitize these documents and extract relevant data such as policy numbers, customer information or claim details. This speeds up the claims settlement and policy management process. In addition, automation in document processing supports risk assessment and fraud detection by revealing patterns and irregularities in the application and claims data. This leads to more accurate and faster decisions, allowing insurance companies to save time and resources while improving their services to customers.
Healthcare: In doctors’ offices and hospitals, countless forms and medical notes are filled out by hand every day. ICR makes it possible to quickly digitize this complex information and capture it in a single database. This contributes to optimal and accurate data capture in healthcare and improves patient care through more efficient access to patient data.
Procure-to-Pay (P2P): Companies offering software solutions in the P2P space can benefit from ICR technologies by automating the process of invoice processing and data capture from purchase requisitions. ICR can help extract the data from incoming paper invoices and automatically integrate it into the accounting system. This significantly speeds up the reconciliation of purchase orders, receipt of goods and subsequent payment approval. The shortened procurement cycle leads to better liquidity planning and optimized cash flow. As a result, P2P software providers offer their customers a more efficient, error-free and cost-effective solution.
endless possibilities.
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Who should consider the use of Intelligent Character Recognition (ICR)?
Pure Optical Character Recognition (OCR) systems are based on templates or rules. Therefore, OCR often requires additional human supervision. OCR merely transcribes a document and provides you with a textual representation of the image, without providing the necessary content for downstream processes. Another area where OCR can fall short is in processing different types of documents.
So if your company or organization works with many document types, different types of forms, handwritten documents or other nuances and variations such as unstructured data, investing in ICR makes sense and will significantly improve your data preparation for efficient document management (DM). Although ICR is a larger investment compared to OCR, the contextual character recognition capabilities of ICR ensure that more complex tasks related to understanding written text can be performed. This forms the basis for further automation.
ICR-supported document processing as a complete solution with our IDP platform
Our intelligent document processing (IDP) integrates various types of artificial intelligence (AI), including machine learning (ML), natural language processing (NLP) and forms of optical character recognition (OCR) and intelligent character recognition (ICR). With the help of our AI-supported software, we dedicate ourselves specifically to your individual challenges in document processing. Find out more about the possibilities of our pioneering solution by clicking here to book a demo. We look forward to hearing from you!
