Today, let's talk about computer vision, also known as CV (Computer Vision).
CV is a technology that allows computers to "read" and "understand". Humans perceive the environment through their eyes, and CV allows computers to imitate human visual systems, and perceive the environment by equipping computers with eyes (cameras) and brains (algorithms), so that it can recognize and understand objects, faces, words, scenes, etc. in images.
Although CV technology has been relatively mature and has been widely used in many fields, it is still technically difficult to deal with complex images and scenes.
Take Optical Character Recognition (OCR), for example, which is a task that recognizes and converts characters in images into text. It sounds simple, but in practical applications, it is difficult to ensure the accuracy of character recognition due to factors such as the complexity of images, visual diversity, and data quality.
For example, in the 32-bit inkjet code recognition of cigarettes, it is difficult and laborious to recognize by the naked eye due to the complex background of the cigarette box and the frequent reflection phenomenon, coupled with the fact that the inkjet code may have problems such as scraping, blurring, and distortion. This is where OCR technology comes in handy.
Traditional general-purpose OCR recognition usually includes steps such as image input, preprocessing, text extraction, and text recognition, and its core lies in separating the text in the image from the background through preprocessing and text extraction, so as to carry out subsequent text recognition. This method is more suitable for simple printed character recognition and cannot handle complex backgrounds, so the accuracy of single word recognition of 32-bit inkjet codes is only 50-80%.
Capernaum AI's monopoly inspection intelligent auxiliary equipment solves this problem through self-developed OCR algorithms. According to the characteristics of cigarette special coding, the deep neural network model is designed, and the industry's only full-spectrum color lamp and multi-band birefringence filter technology are used to adapt to various complex background and lighting conditions, self-match the optimal light, highlight the text area, save the cumbersome preprocessing and text extraction steps in the traditional OCR technology, and simplify the entire recognition process to "image input, text detection, and text recognition", directly locate and recognize the text, and realize the accuracy of 32-bit inkjet code recognition for cigarettes of 9998%。
In terms of recognition, Capernaum AI*** recognition system can quickly capture a single image by training a complex scene recognition model based on meta-learning, use deep learning algorithms to locate and segment text areas, identify a single character, and verify, format and understand the characters to ensure the accuracy and completeness of the output data, realize intelligent extraction of information within 2 seconds, and identify text information in different formats and fonts of different manufacturers. Even in the face of font wear or unclear printing, it can still maintain a high recognition rate of 95%.
Capernaum AI has achieved leading algorithms and high recognition accuracy in the field of OCR, thanks to in-depth research on computer vision and rich experience in commercial applications. The company focuses on the research of multi-modal and multi-task general artificial intelligence technology in complex scenarios, and independently develops a highly autonomous and reliable visual pre-trained large model (VPLM), which is trained and tuned by accessing a dedicated dataset for specific scenarios to generate customized models that meet the needs of various scenarios, ensuring the uniqueness and competitiveness of the product.
With the continuous advancement of CV technology, Capernaum AI will also bring breakthrough solutions in more fields to promote intelligent transformation.