Apple is one of the most influential and recognisable brands in the world, responsible for the rise of the smartphone with the iPhone. Valued at over $2 trillion in 2021, it is also the most valuable...

Context

This dataset was recorded as part of an investigation into machine learning algorithms for iOS. 20,136 glyphs were drawn by 257 subjects on the touch screen of an iPhone 6.

An iOS app was developed to record the dataset. Firstly, subjects entered their age, sex, nationality and handedness. Each subject was then instructed to draw the digits 0 to 9 on the touchscreen using their index finger and thumb. This was repeated four times for each subject resulting in 80 glyphs drawn per subject, 40 using index finger and 40 using thumb. The sequence of glyph entry was random. Instructions to the user were provided using voice synthesis to avoid suggesting a specific glyph rendering.

The index finger and thumb were both used to account for situations in which the subject may only have one hand free. The aim here was to train a model that could accurately classify the glyph drawn in as many real life scenarios as possible.

Cubic interpolation of touches during gesture input was rendered on the screen to provide visual feedback to the subject and to compute arclengths. The screen was initially blank (white) and the gestures were displayed in black. The subject could use most of screen to draw with small areas at the top and bottom reserved for instructions/interactions/guidance. The subject was permitted to erase and repeat the entry, if desired.

Content

https://raw.githubusercontent.com/PhilipCorr/numeral-gesture-dataset/master/database.png" alt="Database Schema">

The database consists of 4 tables as seen in the schema. The tables are Subject, Glyph, Stroke and Touch. This is a logical structure as each subject draws 80 glyphs, each glyph consists of a number of strokes and each stroke consists of a number of touches. The four tables are presented in csv format and sqlite format.

Note that, in the files below, all columns start with a capital Z. This is automatically prepended to column names by Core Data, apples database framework. Column names which start with Z_ were automatically created by Core Data and hence, do not appear in the schema above.

The tables are connected through the first column in each table (Z_PK). This primary key links to the relevant column name in the next table. For example, the subject that entered any given glyph can be found by taking the value from the ZSUBJECT column in the glyph table and finding the matching Z_PK value in the subject table.

Some questions to get you started...

• What is the best model for classifying glyphs?
• What is the best model for classifying sequences of these glyphs?
• What is the best model to predict what number a glyph is before completion?
• How much of the glyph needs to be completed before a prediction can be made?
• What is the best method for interpolating between the touches in the dataset?
• How can a trained model be integrated into iOS apps?

CITATION REQUEST

Please cite the following paper in any publications reporting on use of this dataset:

Philip J. Corr, Guenole C. Silvestre, Chris J. Bleakley Open Source Dataset and Deep Learning Models for Online Digit Gesture Recognition on Touchscreens Irish Machine Vision and Image Processing Conference (IMVIP) 2017 Maynooth, Ireland, 30 August-1 September 2017 http://arxiv.org/abs/1709.06871

Percentage of smartphone users by selected smartphone use habits in a typical day.

Context

Apple has become a household name now a days as many people are using Apple products such as Iphone, Ipad, Apple watch etc. Apple recently became the only company to hit the 2 Trillion dollar mark which is a really great feat. But to be this big Apple had to start somewhere, even in stock market. So here we have the complete Data of Apple stock from its start from 1980 to 2020.

Content

This data set has 7 columns with all the necessary values such as opening price of the stock, the closing price of it, its highest in the day and much more. It has date wise data of the stock starting from 1980 to 2020.

iBeta Level 2 Dataset: Presentation Attacks Liveness Detection & Verifications

The dataset consists of more than 32,300 spoofing attacks of 6 different types specifically curated for a passing iBeta Level 2 and getting a certification. It is compliant with the ISO 30107-3 standard, which sets the highest quality requirements for biometric testing and attack detection solutions.

By geting the iBeta Level 2 certification, biometric technology companies demonstrate their commitment to developing robust and reliable biometric systems that can effectively detect and prevent fraud - Get the data

Attacks in the dataset

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F22059654%2F352f8d1d97a5c1eb03967775f60fdcdb%2FFrame%20126%20(1).png?generation=1725872087101598&alt=media" alt="">

This dataset is designed to evaluate the performance of biometric authentication and identity recognition technology in detecting presentation attacks, it includes different pad tests.

Videos in the dataset: 1. Real Person: real videos of people 2. 2D Mask: printed photos of people's faces cut out along the contour 3. 2D Mask with Eyeholes: printed photos of people with holes for eyes 4. Latex Mask: latex masks on people 5. Wrapped 3D Mask: 3D cardboard mask attached to a mannequin 6. Silicone Mask: silicone masks on people

💵 Buy the Dataset: This is a limited preview of the data. To access the full dataset, please contact us at https://unidata.pro to discuss your requirements and pricing options.

Metadata for the dataset

Devices: Mi10s, Google Pixel 4, Samsung Galaxy A03s, iPhone 11, iPhone SE 2

Resolution: 1920 x 1080 and higher https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F22059654%2F4ee72191bdbc72d18148ebf79a2bd591%2FFrame%20127.png?generation=1725878560196276&alt=media" alt="">

The iBeta Level 2 dataset is an essential tool for the biometrics industry, as it helps to ensure that biometric systems meet the highest standards of anti-spoofing technology. This dataset is used by various biometric companies in various applications and products to test and improve their biometric authentication solutions, face recognition systems and facial liveness detection methods.

🌐 UniData provides high-quality datasets, content moderation, data collection and annotation for your AI/ML projects

In the first quarter of its 2025 fiscal year, Apple generated around ** billion U.S. dollars in revenue from the sales of iPhones. Apple iPhone revenue The Apple iPhone is one of the biggest success stories in the smartphone industry. Since its introduction to the market in 2007, Apple has sold more than *** billion units worldwide. As of the third quarter of 2024, the Apple iPhone’s market share of new smartphone sales was over ** percent. Much of its accomplishments can be attributed to Apple’s ability to keep the product competitive throughout the years, with new releases and updates. Apple iPhone growth The iPhone has shown to be a crucial product for Apple, considering that the iPhone’s share of the company’s total revenue has consistently grown over the years. In the first quarter of 2009, the iPhone sales were responsible for about ********* of Apple’s revenue. In the third quarter of FY 2024, this figure reached a high of roughly ** percent, equating to less than ** billion U.S. dollars in that quarter. In terms of units sold, Apple went from around **** million units in 2010 to about *** million in 2023, but registered a peak in the fourth quarter of 2020 with more than ** million iPhones sold worldwide.

This dataset contains recaptured identity documents from the BID dataset, originally published in the paper "BID dataset: a challenge dataset for document processing tasks". The purpose of this dataset is to aid the domain of recaptured identity document detection.

This is version 1 of the dataset and it consists of three zip files - v1-screen-recaptures.zip. This file contains the identity documents recaptured from a computer monitor using two iPhone models (iphone8 and iPhone12) - v1-printed-document-recaptures.zip. This file contains the same identity documents are above, but printed using an inkjet and laser printer, and recaptured using two iPhone models (iphone8 and iPhone12) - v1-plastic-covered-recaptured.zip. Contains the same printed recaptures as above, but with a plastic cover used in an attempt to obscure surface details.

The filenames used here are the same used in the BID dataset.

Version 1 of this dataset is used in the research paper An Investigation into the Application of the Meijering Filter for Document Recapture Detection. Please cite version 1 as follows:

@inproceedings{magee_2023, location = {Manchester, UK}, title = {An Investigation into the Application of the Meijering Filter for Document Recapture Detection. 12th International Conference on Intelligent Information Processing (ICIIP 2023).}, isbn = {978-1-4244-4295-9}, eventtitle = {12th International Conference on Intelligent Information Processing (ICIIP 2023)}, author = {Magee, John, and Sheridan, Stephen and Thorpe, Christina.}, url = {https://arrow.tudublin.ie/scschcomcon/400/}, year={2023}, }

1,417 People 3D Living Face & Anti-Spoofing Dataset – Multi-Scene, Multi-Light. The collection scenes include indoor and outdoor scenes. The dataset includes males and females. The age distribution ranges from juvenile to the elderly, the young people and the middle aged are the majorities. The device includes iPhone X, iPhone XR. The data diversity includes various expressions, facial postures, anti-spoofing samples, multiple light conditions, multiple scenes. This data can be used for tasks such as 3D face recognition, liveness detection, and anti-spoofing AI model.

Introducing the English Product Image Dataset - a diverse and comprehensive collection of images meticulously curated to propel the advancement of text recognition and optical character recognition (OCR) models designed specifically for the English language.

Dataset Contain & Diversity

Containing a total of 2000 images, this English OCR dataset offers diverse distribution across different types of front images of Products. In this dataset, you'll find a variety of text that includes product names, taglines, logos, company names, addresses, product content, etc. Images in this dataset showcase distinct fonts, writing formats, colors, designs, and layouts.

To ensure the diversity of the dataset and to build a robust text recognition model we allow limited (less than five) unique images from a single resource. Stringent measures have been taken to exclude any personally identifiable information (PII) and to ensure that in each image a minimum of 80% of space contains visible English text.

Images have been captured under varying lighting conditions – both day and night – along with different capture angles and backgrounds, to build a balanced OCR dataset. The collection features images in portrait and landscape modes.

All these images were captured by native English people to ensure the text quality, avoid toxic content and PII text. We used the latest iOS and Android mobile devices above 5MP cameras to click all these images to maintain the image quality. In this training dataset images are available in both JPEG and HEIC formats.

Metadata

Along with the image data, you will also receive detailed structured metadata in CSV format. For each image, it includes metadata like image orientation, county, language, and device information. Each image is properly renamed corresponding to the metadata.

The metadata serves as a valuable tool for understanding and characterizing the data, facilitating informed decision-making in the development of English text recognition models.

Update & Custom Collection

We're committed to expanding this dataset by continuously adding more images with the assistance of our native English crowd community.

If you require a custom product image OCR dataset tailored to your guidelines or specific device distribution, feel free to contact us. We're equipped to curate specialized data to meet your unique needs.

Furthermore, we can annotate or label the images with bounding box or transcribe the text in the image to align with your specific project requirements using our crowd community.

License

This Image dataset, created by FutureBeeAI, is now available for commercial use.

Conclusion:

Leverage the power of this product image OCR dataset to elevate the training and performance of text recognition, text detection, and optical character recognition models within the realm of the English language. Your journey to enhanced language understanding and processing starts here.

The dataset created at the 2024 National Festival in Portugal consists of 9062 images. It is a dataset designed for recognizing objects from the YCB dataset along with detecting people. It comprises 1500 images of people from the COCO dataset, 416 images taken in the arena with the Astra camera of the TIAGo robot, 2146 images taken with an iPhone, and the rest of the images are from the @Home Objects Dataset.

Infant Crying smartphone speech dataset, collected by Android smartphone and iPhone, covering infant crying. Our dataset was collected from extensive and diversify speakers(201 people in total, with balanced gender distribution), geographicly speaking, enhancing model performance in real and complex tasks. Quality tested by various AI companies. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.

The number of smartphone users in the United States was forecast to continuously increase between 2024 and 2029 by in total 17.4 million users (+5.61 percent). After the fifteenth consecutive increasing year, the smartphone user base is estimated to reach 327.54 million users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smartphone users in countries like Mexico and Canada.

10 People - 3D&2D Living_Face & Anti_Spoofing Data. The collection scenes is indoor scenes. The dataset includes males and females. The age distribution ranges from juvenile to the elderly, the young people and the middle aged are the majorities. The device includes iPhone X, iPhone XR. The data diversity includes various expressions, facial postures, anti-spoofing samples, multiple light conditions, multiple scenes. This data can be used for tasks such as 3D face recognition, 3D Living_Face & Anti_Spoofing.

Noise pollution in cities has major negative effects on the health of both humans and wildlife. Using iPhones, we collected sound-level data at hundreds of locations in four areas of Boston, Massachusetts (USA) before, during, and after the fall 2020 pandemic lockdown, during which most people were required to remain at home. These spatially dispersed measurements allowed us to make detailed maps of noise pollution that are not possible when using standard fixed sound equipment. The four sites were: the Boston University campus (which sits between two highways), the Fenway/Longwood area (which includes an urban park and several hospitals), Harvard Square (home of Harvard University), and East Boston (a residential area near Logan Airport). Across all four sites, sound levels averaged 6.4 dB lower during the pandemic lockdown than after. Fewer high noise measurements occurred during lockdown as well. The resulting sound maps highlight noisy locations such as traffic intersections and qui..., We collected sound measurements within four different urban sites in Boston, Massachusetts. Working in small teams of 2-4 people, we used the mobile app SPLnFFT to collect sound level data in A-weighted decibel readings using smartphones. We exclusively used iPhones for data collection for consistency in hardware and software. Before each collection, we calibrated each iPhone to the same standard, which was used for every collection outing. We recorded the L50 value (the median sound level) for each recording because the L50 value is less affected by short bursts of loud sound than the mean reading. Recordings ran for approximately 20 seconds each. We recorded all sound measurements between 9 am and 5 pm on workdays to avoid the influence of rush-hour traffic, and only collected data on days without rain, snow, or strong wind to prevent inaccuracies due to weather. Within these conditions, we collected sound measurements over multiple days and at different times to ensure representative..., , # Data from: Maps made with smartphones highlight lower noise pollution during COVID-19 pandemic lockdown at four locations in Boston

https://doi.org/10.5061/dryad.ncjsxkt35

Dataset contents include csv files of all data (each file describes collection year and site of data), R script used to create noise maps, and kml files needed to run the map creation code.

Description of the data and file structure

Each csv file contains the L50 values (median sound level) taken from hundreds of 20 second recordings over multiple collection days. The SPLnFFT application exports the latitude and longitude of where the recording was taken, which is also included in the csv files and is used to create the noise maps. The csv files are used as data frames for the R script to create noise maps for each collection site. The R script contains comments and instructions to clearly indicate each step of the map creation. The kml files are used to create bound...

4,458 People - 3D Facial Expressions Recognition Data. The collection scenes include indoor scenes and outdoor scenes. The dataset includes males and females. The age distribution ranges from juvenile to the elderly, the young people and the middle aged are the majorities. The device includes iPhone X, iPhone XR. The data diversity includes different expressions, different ages, different races, different collecting scenes. This data can be used for tasks such as 3D facial expression recognition.

The number of smartphone users in the Philippines was forecast to increase between 2024 and 2029 by in total 5.6 million users (+7.29 percent). This overall increase does not happen continuously, notably not in 2026, 2027, 2028 and 2029. The smartphone user base is estimated to amount to 82.33 million users in 2029. Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smartphone users in countries like Thailand and Indonesia.

The smartphone penetration in the Philippines was forecast to continuously decrease between 2024 and 2029 by in total 6.4 percentage points. According to this forecast, in 2029, the penetration will have decreased for the fourth consecutive year to 65.75 percent. The penetration rate refers to the share of the total population.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the smartphone penetration in countries like Laos and Malaysia.

The number of mobile broadband connections in the Philippines was forecast to continuously increase between 2024 and 2029 by in total 18.3 million connections (+20.46 percent). After the ninth consecutive increasing year, the number of connections is estimated to reach 107.69 million connections and therefore a new peak in 2029. Mobile broadband connections include cellular connections with a download speed of at least 256 kbit/s (without satellite or fixed-wireless connections). Cellular Internet-of-Things (IoT) or machine-to-machine (M2M) connections are excluded. The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of mobile broadband connections in countries like Vietnam and Laos.

The number of smartphone users in Malaysia was forecast to continuously increase between 2024 and 2029 by in total 1.9 million users (+5.62 percent). After the fifteenth consecutive increasing year, the smartphone user base is estimated to reach 35.72 million users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find further information concerning Indonesia and Singapore.