AI OCR is a high-speed character recognition solution specifically designed for industrial applications. It combines photomechanical integration for streamlined operations, enabling immediate use upon turning it on. Designed to facilitate real-time detection at rates up to 600 images per minute, AI OCR efficiently handles variable industrial scenarios, such as low contrast, reflective surfaces, and worn out/broken characters. The entire model training and deployment process is simplified, making AI model application an effortless task.

The Equipment Management Module integrates outdated machinery information through AI image recognition technology, providing real-time status checks and production data analysis. This solution offers various recognitions - instrumental readings, on-screen visuals, and work station orders – allowing users to manage productivity with ease. It swiftly responds to anomalies and clearly displays production data and processes for precise control.

The Smart Dashboard Recognition solution leverages AI to discern readings on various machinery within traditional manufacturing settings. With machines of different ages and models, some factories encounter problems of system compatibility and complex backend management. Sometimes, manual data transcribing and reporting can lead to errors. This solution, through deep learning, identifies numbers on the dashboard and reports the data to the backend system in real-time, reducing human errors, and enhancing workflow and productivity.

Aimed at automating document processing in logistics, this solution uses computer vision to read documents effectively and prevent manual data entry into ERP or WMS systems. It automates the cross-checking between documents and physical inventory or system data, and it can extract relevant information from paper documents or PDFs. The use of this solution can lead to smarter document handling that is up to 10 times faster, prevents human errors and allows better control over the process. It can be integrated as a part of various workflows and can also be white-labelled as part of your own solutions.

Klippa's Mobile Utility Meter Scanning SDK leverages AI to revolutionize the meter reading process. It offers swift scanning of a variety of meters such as gas, water, electricity, and oil. The system enhances customer experience by enabling remote meter reading without the need for technician appointments. It replaces error-prone manual processes with faster, automated data extraction. Ensuring security and compliance, the SDK requires no internet connection for operation, courtesy of local OCR.

Providing fast and flexible scanning capabilities, this solution uses computer vision to smartly scan everything from classic barcodes, complex labels to text printed on boxes, even labels without barcodes or QR codes. It is capable of extracting additional data like lot numbers or expiry dates, drastically saving time and eliminating manual work. It can be effortlessly integrated as part of various workflows like inbound and outbound checks, cycle counts and can also be white-labelled as part of your own solutions such as WMS, drones, mobile applications or others.

Mobile Document Scanning SDK by Klippa streamlines document capture and data extraction process using AI technology. The solution can handle a wide range of documents such as medical, financial, identity, legal, logistics, and HR documents. The software combines the power of OCR and AI to offer high efficiency and accuracy, and assures compliance with GDPR rules. User-friendly and customizable, it delivers real-time user feedback, fast data extraction, and easy implementation due to the well-documented SDK.

Amantya's Smart OCR uses AI-powered Optical Character Recognition (OCR) to convert unstructured data into actionable business intelligence. The system has been engineered to securely capture, extract, and transform data from documents like receipts, bills, invoices, folios, and more, into editable and searchable data forms. Offering services such as online product data capture, medical receipts scan, handwritten data transformation, scanned image interpretation, and more, it can work seamlessly with non-formatted documents and provide high-quality processing in less than 3 seconds.

Anyline's Tire Sidewall Scanner uses the camera-enabled mobile device to read tire identification numbers, tire size, make, and model from tire sidewalls. Key details like age, specifications, and production location of tires can be recorded swiftly, cutting record-keeping time from minutes to seconds. The solution is designed to reduce human errors and speed up the tire check process five times. It boasts features such as automatic orientation, batch processing, and continuous scanning.

VIN Scanner SDK is designed to enhance fleet management, transforming smartphones and tablets into portable VIN code scanners. This tool allows effortless data capture even under poor lighting and condition of barcodes. It offers lightning-fast and precise VIN detection, scanning even through windshields and dealing proficiently with barcode damages or distortion. An entirely offline solution, it captures accurate key-value pairs for a reliable data extraction and optimizes record-keeping in fleet management. It can cater to all scanning requirements, making it a one-stop solution.

Document Scanner SDK converts mobile devices into high-quality document scanners. The solution makes use of advanced computer vision algorithms and machine learning models to achieve proficient and high-speed scanning of documents, regardless of the conditions. The SDK can be integrated into settings with a self-explanatory User Interface and works with all camera-equipped devices. All data processing happens offline on the user’s device, ensuring maximum data security.

ScanForm offers a scalable data collection solution that transcribes handwritten forms into digital data with a single smartphone photo, saving time and reducing human errors. It leverages artificial intelligence to extract all the information, and calculates custom summary statistics alongside visual analytics instantly. The platform vastly cuts down on data extraction and typing costs, where 30-40% of budgets can often be allocated. With a self-learning Optical Character Recognition (OCR) accuracy of over 98%, it is designed to understand local handwriting styles. The captured system works offline as well and uploads the data once a network is available.

Cognex In-Sight vision systems with deep learning OCR solutions can confirm that lids and containers match each other and accurately reflect the contents of the package, as well as confirm that labels comply with internal procedures and quality standards enforced by regulatory agencies. Cognex technology ensures high-speed reading and decoding of barcodes and text in the most demanding environments.

Optical Character Recognition (OCR) and Optical Character Verification (OCV) capabilities read and confirm printed data, verifying the quality of assorted elements required to be imprinted, such as logos, dates/batch numbers, and graphics. Comprehensively utilized in Cognex's versatile In-Sight Systems, these code reading tools ensure precise decoding accuracy.

Cognex Vision Systems paired with OCR technology can detect barcodes' existence and verify letter-number sequences, catering to stringent OCR demands inclusive of laser-etched marks or DPM texts. Cognex Deep Learning Solutions secure precise reading and authentication of barcodes. Moreover, deep learning offers OCR and character verification (OCV) functionalities that encode distorted, slanted, and poorly etched letters, given a pretrained multidirectional font database. No requirement exists for designing extra programs or font training since it readily identifies most textual content.

Cognex tire solutions use character reading vision tools to enable tire manufacturers to read codes with high accuracy even in the most demanding conditions. The character reading vision tools can accurately locate and read DOT characters, and adapt to the changing code appearance caused by variations in the molding process.

By using the In-Sight vision system, food and beverage manufacturers can ensure that labels are placed in the correct position on the product and avoid product recalls due to quality issues or damage to brand reputation. Similarly, automated inspection can identify incorrect labels before they cause further problems along the supply chain due to misaligned labels. Label alignment inspection can be used in conjunction with optical character recognition (OCR) and other In-Sight vision tools to ensure overall label readability and compliance.

Cognex Deep Learning can read printed barcodes on difficult backgrounds. With a pre-trained font library, the deep learning OCR tool is easy to set up and deploy. It can then be trained on a small set of images of text printed on a variety of backgrounds, and it learns to identify the text while ignoring the background. This is even the case when the text appears on new backgrounds that were not in the original training set. When text appears on a new background, the OCR tool does not need to be retrained, which keeps the production line running without interruption or loss of read accuracy.

Tires go through many high-pressure, high-load and high-temperature processes during the manufacturing process. The dust generated by the on-site machines and materials, coupled with the long-term operation of the printing process, make the surface of the inner tube blurry and the color shades uneven, affecting the recognition of the inner tube coding. After mass production, it is not conducive to manual recognition. If traditional AOI detection is used, it cannot be effectively identified in the case of unclear digital printing.Using Solomon SolVision's Segmentation technology, the numbers and shapes of the tire inner tube codes are photographed and trained for AI models. Then, optical character recognition (OCR) is used to accurately identify the codes. Even for incompletely printed or lightly colored characters, they can all be successfully identified, effectively improving the accuracy of code recognition.

Cognex Deep Learning can read printed OCR codes on flexible plastic film, which are not only difficult to read, but the background can also vary due to many different cut parts or chicken. The deep learning OCR tool comes with a pre-trained font library, making it easy to set up and deploy. The OCR tool is trained on a small set of images of text that is skewed, angled, and distorted. After that, it can find and read this type of text on flexible food packaging, regardless of the product underneath the packaging.

Engine numbers are printed on the engine by branding. When taking images of engine numbers, they are also easily affected by shadows, resulting in uneven brightness of fonts and backgrounds, which makes it impossible to read the numbers by machine. Even with artificial visual inspection, it is still difficult to quickly identify the codes on the engine on the production line.Using the Segmentation technology of Solomon SolVision AI image platform, the model is trained with image samples of different brightness and optical character recognition (OCR) is performed to convert the engine number in the image into numerical information, which is immediately logged into the original database system and linked to the VIN.

Use SolVision's Feature Detection feature to learn the location points that need to be identified on the tray, and then use Segmentation technology to perform optical character recognition (OCR), which can greatly optimize the traditional AOI workflow. It is not restricted by the displacement, skew and character defects of the identification screen, and can accurately identify the source of individual materials. With the increase of the number of learning pieces, the ability of AI to identify characters can also be continuously optimized, making character identification no longer difficult.

Traceability solutions ensure full compliance with food safety and traceability regulations by capturing images of codes at each scanning point and storing the decoded data in a central database. Cognex barcode readers can reliably read 1D and 2D bar code images with a 99.9% read rate, regardless of code quality or orientation. Image-based readers offer the speed and accuracy needed to ensure that all shapes and sizes of packages are properly sorted, picked, stored or shipped, and easily identified and located in the event of a product recall. In-Sight vision systems use AI-based OCR tools to read alphanumeric date/lot codes and store the information in a central database that can track and trace products throughout the supply chain.

Combining smart cameras with deep learning software uses optical character recognition (OCR) to decode damaged barcodes. Thanks to the pre-trained font library of deep learning, the deep learning barcode reading tool in the software is ready to use out of the box, greatly shortening the development time. Users only need to define the target detection area and set the character size. When introducing new characters, you don't need to have visual expertise, and you can also retrain this robust tool to read application-specific barcodes that traditional OCR tools can't decode.

Cognex Deep Learning's text and character reading capabilities can reliably and accurately decode deformed, skewed, damaged, or low-contrast codes. This can be done by training on a set of OCR code images with different angles, light sources, damage conditions, and other variations.

SolVision's Segmentation technology performs optical character recognition (OCR), which is different from the traditional AOI workflow. It is not limited by the object background color, ambient light and multiple character types. It can accurately identify individual codes, and with the increase of the number of learning samples, it can also continuously optimize the AI's ability to identify characters, making character recognition no longer difficult.

Cognex's Deep Learning OCR tool can use a pre-trained built-in font library of over 1,000 characters to read curved strings, low contrast characters, as well as distorted, skewed, and poorly etched barcodes. The OCR tool also provides a re-training capability, allowing users to solve new or specific characters that cannot be automatically identified on the first pass. Quickly and accurately reading chip identification numbers not only improves traceability, but also ensures the correct information is captured for future reference when needed.

1. Cognex Deep Learning quickly and reliably solves Printed Circuit Board (PCB) assembly verification challenges by undergoing training with sets of qualified vs unqualified PCB images. Three distinct deep learning tools operate seamlessly together on a single workstation for uniform testing of circuit boards without causing delays in production.
2. Assembly Verification Tools check if all components appear correctly positioned; meanwhile, Defect Detection Tools mark any solder problems, damaged locations on board-mounted components or other flaws. OCR (Optical Character Recognition) Tools read all characters on circuit boards and component surfaces, outputting them as text strings.

Matrox MIL10 SureDotOCR™ character recognition tool is specially developed for challenging dot matrix text printed by inkjet and dot printers. It can be calibrated according to the specified dot size, and can be used for text distortion, uneven background and different light conditions. Recognition.

The software can quickly locate and read solid text in images, and can tolerate a certain degree of tilt and contrast changes. It is flexible enough to overcome the problem of character building and background blur. On the other hand, if the text is clear enough, String reader will perform the detection, and if the text is accidentally covered, Matrox DA can also automatically switch to the existence/non-existence inspection mode, which can effectively respond to changes in the production line.

By using the Segmentation technology of SolVision AI image platform, AI models are trained with image samples of bottle cap text and barcodes, and optical character recognition (OCR) is performed. This can accurately identify product information on the outer packaging in the high-speed beverage production line. In addition to detecting products with poor printing, it also greatly enhances the efficiency of traceability management and record retention on the production line.

1. Full Chinese operation interface
2. Quick adjustment page for product fine-tuning
3. Statistical functions (total inspected/qualified/defective quantities)
4. Defective image storage and classification
5. Ability to adjust settings during inspection
6. Complete rejection mechanism planning (low/medium/high speed, contact/non-contact type)
7. Equipment can process up to 1200 pcs/minute at maximum speed

The In-Sight vision system, combined with OCRMax technology, can detect the presence or absence of dates and batch codes, and verify the correctness of their alphanumeric chains. For demanding OCR applications, including DPM text that is laser marked, dot peened or chemically etched, Cognex AI OCR tools ensure accurate reading and verification of barcodes. These tools can use OCR and character verification (OCV) to decode deformed, skewed and poorly etched characters. A pre-trained omnidirectional font library can recognize most text without the need to design additional programs or font training.

Cognex's In-Sight 7800 Series Visual Systems boast exceptional accuracy in reading tax stamps' OCR codes, helping tobacco industry manufacturers comply with stringent regulations concerning cigarette taxes. Leveraging the PatMax Object Location Tool, In-Sight 7905 searches for and positions patterns on fiscals stamps, followed by Exposure Distribution Graph and OCR tools to locate characters and decode OCR codes. Operating flawlessly at minimal distances, In-Sight 7905 extracts and examines high-resolution, high-contrast images unaffected by deformation directly from high-speed printers.

At present, manual identification and registration of bicycle frame numbers are still used, which is labor-intensive and inefficient. If automatic optical identification (AOI) is used for character recognition, the stainless steel tube surface is a curved surface, and lighting is easy to cause reflection, making the The code on the curved surface is not clear. Whether manual or automatic optical inspection, it is more difficult to identify the characters on the curved, reflective stainless steel tube. Solomon combines machine vision and artificial intelligence to use SolVision Segmentation technology to train AI models for the gloss of the numbers on the stainless steel tube, which can achieve excellent optical character recognition results.

Cognex DataMan readers employ the combination of 1DMax with Hotbars and 2DMax with PowerGrid algorithms to accurately recognize one-dimensional and two-dimensional codes printed on labels or directly etched onto circuit boards via laser engraving. Leveraging the full potential of machinery requires reliable code identification capabilities provided by Cognex vision systems. Additionally, they offer Optical Character Recognition (OCR) and character verification (OCV) functions, allowing serial number recognition for circuit boards and expensive components, or extracting additional information not included in original barcode tags.

The software only needs engineers to configure the Target Inspection Zone and character size after setup, requiring no need for retraining. Its pretrained font library allows it to decode characters effortlessly, even in extremely difficult-to-read conditions. However, should the situation arise where standard character variants fail, users have the option to retrain the software with multiple customized character alterations for successful decoding.

Epic Systems, a major system integration company, has integrated the Matrox MIL vision library to develop an OCR machine vision system that has been deployed in the manufacturing site of a major food manufacturer. This vision system will instantly identify whether the expiration date on the bottle is correct, and instantly remove the incorrect bottles to prevent defective products from entering the market.

1. Measure various features of turbine blades
2. Inspect blades for defects and flaws
3. Verify that the characters on the parts are correct