By combining high-resolution industrial cameras, precision optical lenses, and professional visual lighting, combined with OCR (Optical Character Recognition) and OCV (Optical Character Verification) technologies, it can rapidly capture textual information from product surfaces, automatically completing recognition, verification, and data output, enhancing product traceability and production automation efficiency. It is widely used in industries such as electronics manufacturing, semiconductors, automotive parts, medical devices, food packaging, and logistics.
OCR
(Optical Character Recognition)
Automatically recognize and convert text in images into editable, searchable digital data to answer 'What content is printed?'
Applicable content
> English, numbers
> Chinese-English mixed characters
> Serial number
> Lot Number／Batch Code
> Manufacturing date
> Validity period
> DPM (Direct Part Mark) – Laser engraving, printing, or dot-engraving of text/barcodes on metal or plastic surfaces, ensuring stable recognition even with low reflection or contrast
OCV
(Optical Character Verification)
Compare the recognized text with the default standard string character by character to confirm whether the print is correct, avoiding printing errors, missing characters, or inconsistent content that leads to the next site.
Detectable Items
> Character Content Errors
> Missing Print / Missing Characters
> Print Misalignment
> Character Overlap
> Font Distortion
> Blurriness / Insufficient Contrast
> Abnormal Character Spacing
OCR Visual Recognition Process
① Image Acquisition
Using high-resolution industrial cameras paired with appropriate lighting to capture high-quality text images, reducing interference from reflections, shadows, and ambient light.
② Image Preprocessing
Through algorithms such as grayscaling, binarization, noise filtering, contrast enhancement, and skew correction, improving the quality and stability of text recognition.
③ Character Localization
Automatically searching for the text region (ROI) and precisely locating the position of each character, adapting to different fonts, sizes, and layouts.
④ OCR Recognition
Using AI deep learning or optical character recognition (OCR) algorithms to analyze character features, completing text recognition and content parsing.
⑤ Verification
Verifying content accuracy based on preset rules, such as character count, format, serial numbers, database comparison, or barcode information consistency.
⑥ Data Output
Recognition results can be output to PLC, MES, ERP, SQL Database, or other automated systems, enabling product traceability and process management.