Difference between revisions of "Character Recognition"
m |
|||
| (16 intermediate revisions by the same user not shown) | |||
| Line 2: | Line 2: | ||
|title=PRIMO.ai | |title=PRIMO.ai | ||
|titlemode=append | |titlemode=append | ||
| − | |keywords=artificial, intelligence, machine, learning, models | + | |keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools |
| − | |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | + | |
| + | <!-- Google tag (gtag.js) --> | ||
| + | <script async src="https://www.googletagmanager.com/gtag/js?id=G-4GCWLBVJ7T"></script> | ||
| + | <script> | ||
| + | window.dataLayer = window.dataLayer || []; | ||
| + | function gtag(){dataLayer.push(arguments);} | ||
| + | gtag('js', new Date()); | ||
| + | |||
| + | gtag('config', 'G-4GCWLBVJ7T'); | ||
| + | </script> | ||
}} | }} | ||
| − | [ | + | [https://www.youtube.com/results?search_query=ocr+image+handwriting+Text+digit+recognition+artificial+intelligence+deep+learning YouTube search...] |
| − | [ | + | [https://www.google.com/search?q=ocr+image+handwriting+Text+digit+recognition+artificial+intelligence+deep+learning ...Google search] |
| + | |||
| + | * [https://www.slideshare.net/DataWorksMD/multilingual-optical-character-recognition-ocr-in-unconstrained-image-and-video Multilingual Optical Character Recognition (OCR) in Unconstrained Image and Video | David Etter] | ||
| + | ** Concepts/Tools/Standards: | ||
| + | *** [[Deep Features]] | ||
| + | *** [[Local Features]] | ||
| + | *** [https://github.com/python-pillow/Pillow Pillow] file format support, an efficient internal representation, and fairly powerful image processing capabilities | ||
| + | *** [https://www.nist.gov/programs-projects/face-recognition-vendor-test-frvt Face Recognition Vendor Test (FRVT) | NIST] | ||
| + | ** [[Datasets]]/Networks: | ||
| + | *** [https://en.wikipedia.org/wiki/ImageNet ImageNet | Wikipedia] | ||
| + | *** [https://wordnet.princeton.edu/ WordNet] | ||
| + | *** [[ResNet-50]] | ||
| + | *** [https://www.robots.ox.ac.uk/~vgg/data/vgg_face/ VGG | Oxford] | ||
| + | *** [https://www.forbes.com/sites/korihale/2019/06/25/microsoft-scraps-10-million-facial-recognition-photos-on-the-low/#6672d61949f2 Microsoft Scraps 10 Million Facial Recognition Photos On The Low | Kori Hale -Forbes] | ||
| + | * [https://www.mediadistillery.com/technology/ Media Distillery] understanding video to engage customers | ||
| + | * [https://cloud.google.com/vision/docs/ocr Detect text in images | Google] - Optical Character Recognition (OCR) | ||
| + | ** [https://opensource.google.com/projects/tesseract Tesseract OCR | Google] | ||
| + | *** [https://www.i2tutorials.com/technology/building-an-ocr-using-yolo-and-tesseract/ Building an OCR using YOLO and Tesseract | i2tutorials] | ||
| + | * [[Textract]] | ||
| + | * [https://im2recipe.csail.mit.edu/ Recommends ingredients and recipes based on a photo of food] | ||
| + | * [https://towardsdatascience.com/a-gentle-introduction-to-ocr-ee1469a201aa A gentle introduction to OCR | Gidi Shperber - Toward Data Science] | ||
| + | |||
| + | <b>Capabilities of AI in Character Recognition:</b> | ||
| + | * Robust Pattern Recognition: AI algorithms, such as convolutional neural networks (CNNs), can effectively recognize patterns and features in characters, making them highly accurate in character identification tasks. | ||
| + | * Handwriting Recognition: AI models can learn to recognize and interpret handwritten characters, facilitating applications such as automated form processing, signature verification, and digital handwriting analysis. | ||
| + | * Multilingual Support: AI-based character recognition can handle diverse languages and character sets, including Latin, Chinese, Arabic, and more, making it applicable to global communication and document processing needs. | ||
| + | * Contextual Understanding: Advanced AI models can utilize contextual information, such as word or sentence context, to improve character recognition accuracy, especially in situations where characters can have multiple interpretations or variations. | ||
| + | |||
| + | <b>Advantages of AI in Character Recognition:</b> | ||
| + | * Accuracy: AI models, when trained on large and diverse datasets, can achieve high accuracy rates in character recognition tasks, outperforming traditional rule-based or template-based methods. | ||
| + | * Adaptability: AI models can learn from new data and adapt to different handwriting styles, fonts, and character variations, making them versatile and capable of handling a wide range of inputs. | ||
| + | * Efficiency: AI-based character recognition can process large volumes of characters quickly, automating labor-intensive tasks and saving significant time and effort. | ||
| + | * Scalability: AI algorithms can scale effectively to handle character recognition tasks in various applications and industries, from document digitization to automated data entry. | ||
| − | + | <b>Potential Impact on Efficiency and Effectiveness:</b> | |
| − | + | * Improved Workflow: AI-powered character recognition systems streamline document processing workflows by automating the extraction of textual information, reducing manual effort and human errors. | |
| − | * | + | * Data Accessibility: AI in character recognition enables efficient extraction and digitization of text from printed materials, historical documents, or handwritten notes, making the information easily searchable and accessible. |
| − | * | + | * Language Processing: AI models can be integrated with natural language processing techniques, enabling advanced text analysis, translation, [[Sentiment Analysis]], and information extraction from recognized characters. |
| − | * | + | * Enhanced User Experience: By accurately recognizing characters, AI systems can improve user experiences in various applications, such as optical character recognition (OCR) in mobile devices, translation apps, and automated data entry systems. |
| − | * | ||
| − | |||
| − | |||
<youtube>ocB8uDYXtt0</youtube> | <youtube>ocB8uDYXtt0</youtube> | ||
| Line 23: | Line 61: | ||
<youtube>Gj0iyo265bc</youtube> | <youtube>Gj0iyo265bc</youtube> | ||
<youtube>HMcx-zY8JSg</youtube> | <youtube>HMcx-zY8JSg</youtube> | ||
| + | <youtube>hmUfGkEHakM</youtube> | ||
Latest revision as of 19:57, 9 July 2023
YouTube search... ...Google search
- Multilingual Optical Character Recognition (OCR) in Unconstrained Image and Video | David Etter
- Concepts/Tools/Standards:
- Deep Features
- Local Features
- Pillow file format support, an efficient internal representation, and fairly powerful image processing capabilities
- Face Recognition Vendor Test (FRVT) | NIST
- Datasets/Networks:
- Concepts/Tools/Standards:
- Media Distillery understanding video to engage customers
- Detect text in images | Google - Optical Character Recognition (OCR)
- Textract
- Recommends ingredients and recipes based on a photo of food
- A gentle introduction to OCR | Gidi Shperber - Toward Data Science
Capabilities of AI in Character Recognition:
- Robust Pattern Recognition: AI algorithms, such as convolutional neural networks (CNNs), can effectively recognize patterns and features in characters, making them highly accurate in character identification tasks.
- Handwriting Recognition: AI models can learn to recognize and interpret handwritten characters, facilitating applications such as automated form processing, signature verification, and digital handwriting analysis.
- Multilingual Support: AI-based character recognition can handle diverse languages and character sets, including Latin, Chinese, Arabic, and more, making it applicable to global communication and document processing needs.
- Contextual Understanding: Advanced AI models can utilize contextual information, such as word or sentence context, to improve character recognition accuracy, especially in situations where characters can have multiple interpretations or variations.
Advantages of AI in Character Recognition:
- Accuracy: AI models, when trained on large and diverse datasets, can achieve high accuracy rates in character recognition tasks, outperforming traditional rule-based or template-based methods.
- Adaptability: AI models can learn from new data and adapt to different handwriting styles, fonts, and character variations, making them versatile and capable of handling a wide range of inputs.
- Efficiency: AI-based character recognition can process large volumes of characters quickly, automating labor-intensive tasks and saving significant time and effort.
- Scalability: AI algorithms can scale effectively to handle character recognition tasks in various applications and industries, from document digitization to automated data entry.
Potential Impact on Efficiency and Effectiveness:
- Improved Workflow: AI-powered character recognition systems streamline document processing workflows by automating the extraction of textual information, reducing manual effort and human errors.
- Data Accessibility: AI in character recognition enables efficient extraction and digitization of text from printed materials, historical documents, or handwritten notes, making the information easily searchable and accessible.
- Language Processing: AI models can be integrated with natural language processing techniques, enabling advanced text analysis, translation, Sentiment Analysis, and information extraction from recognized characters.
- Enhanced User Experience: By accurately recognizing characters, AI systems can improve user experiences in various applications, such as optical character recognition (OCR) in mobile devices, translation apps, and automated data entry systems.
