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OCR Automation in Customs: The Power of AI-based OCR

OCR Automation

OCR Automation in Customs: The Power of AI-based OCR Have you thought about advancing using smart technology? If so, it’s crucial to investigate how technology is extending into novel fields. People and companies always look for new ways to increase efficiency and simplify processes in order to keep up with the rapid advancements in technology. OCR automation (Optical character recognition) is one example of this advanced technique in practice today. OCR automation software has fundamentally altered how businesses manage their data by converting scanned photos and even handwritten text into formats that are readable and editable by computers. Within the context of this blog, we investigate the possibilities of OCR automation and how it may be used to drive efficiency across various industries, especially in customs. What research says about OCR automation: “Research indicates that OCR automation, which mechanically extracts data from documents, can reduce processing times in customs by as much as 70%.” This results in turnaround times decreasing from days to just minutes, translating to huge efficiency gains. Additionally, there is a significant improvement in data capture accuracy, which reduces errors and discrepancies. In the end, these developments result in cost savings of over 70% as compared to manual processing. These results show how OCR automation has a wide range of applications that can optimise processes and yield substantial benefits for many departments in your company. Within the context of this blog, we investigate the possibilities of OCR automation and how it may be used to drive efficiency across various industries, especially in customs. What is OCR automation? The optical character recognition system is software that is built to convert the hard forms of documents and photographs into digital systems. But when we align it, like automating OCR, it is state-of-the-art software that quickly transforms scanned documents or photos into text that can be edited on a computer. Automated OCR software can recognise and interpret characters in photographs, such as those obtained from handwritten text or low-resolution scans, as a result of enhanced algorithms and machine learning capabilities. Reducing manual data entry with OCR automation boosts industry efficiency, accuracy, and productivity. It’s useful in many different fields, including document management, accounting, healthcare, retail, law, and more, because it helps businesses streamline processes, save money, and make their data more easily accessible. Businesses can digitise paper documents with OCR automation to fully benefit from digital data and the advantages of an increasingly automated and networked future. What problems do we face without OCR in import and export? Although OCR is used in almost every working field in the form of automated OCR software. Our main focus is in the customs field, so let’s turn ourselves into the customs world. However, OCR had a wall that blocked a further successful path. It faced several difficulties, such as: There was still a lot of manual documentation work while importing and exporting, which interpreted multiple errors. The processing time was very slow because adding the documents to the scanning process took longer. The customs freight process faces multiple rejections from HMRC due to errors. Digital platforms and technologies automate and integrate modern import and export procedures. Manual data entry may cause inefficiency and inaccuracies in these systems. Manual data entry allows data tampering and fraud. OCR improves data integrity and reduces fraud. Manual data entry becomes unsustainable as import and export quantities rise. OCR can scale without compromising accuracy. Different OCR automation types used in customs Power automated OCR classifies its use cases into subcategories, such as the following: Deep learning-based OCR Thanks to recent developments in deep learning and AI, OCR models that can be trained on massive datasets have achieved a new level of accuracy. OCR systems powered by deep learning are flexible enough to accommodate numerous scripts, languages, and writing styles. Intelligent word recognition (IWR) IWR is an enhanced optical character recognition method. For more precise results when processing complex or cursive handwriting, it can comprehend the meaning of sentences and the connections between characters. Optical word recognition (OWR) OWR is optimised for word recognition rather than character recognition. It’s especially helpful in cases where the text’s format, typeface, or layout might change. Handwritten OCR Handwriting OCR stands for “optical character recognition” and is used to refer to the process of digitising handwritten text. It is useful for digitising documents and notes written by hand since it employs complex algorithms to evaluate and understand different types of handwriting. Standard OCR This form of optical character recognition is made to read text from scanned files. When used with legible, well-defined fonts, it successfully translates handwritten text into machine-readable code. Zonal OCR In zonal OCR, OCR analysis is performed only on an image’s or document’s defined regions (zones). This method comes in handy when OCR processing needs to be customised for various parts of a document. Matrix OCR Characters in grid format images, such as those found in spreadsheet tables or forms, can be read using matrix OCR. Data collection and form processing systems typically employ it. Mobile OCR Mobile OCR is designed for handheld devices like smartphones and tablets, facilitating the capture and extraction of text from photos. It is used in augmented reality and mobile scanning apps. Extraction of Stable Field OCR This sort of optical character recognition is made for picking out things like names, dates, addresses, and invoice numbers from paper documents. It has widespread use in document and data management programs. Benefits of OCR automation in customs 1. The OCR automation speeds up the conversion of photos or scanned documents into editable text, decreasing manual data entry. 2. Automation OCR techniques and machine learning improve the accuracy of character recognition and data extraction. Automation improves data quality and reliability. 3. Businesses can cut data entry expenses by automating text extraction. It also saves money by eliminating document storage. 4. OCR automation digitises documents, making them searchable. Data management and retrieval improve. 5. Integrating OCR automation into existing systems speeds data movement and analysis between applications

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