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...

The number of Apple iPhone unit sales dramatically increased between 2007 and 2023. Indeed, in 2007, when the iPhone was first introduced, Apple shipped around *** million smartphones. By 2023, this number reached over *** million units. The newest models and iPhone’s lasting popularity Apple has ventured into its 17th smartphone generation with its Phone ** lineup, which, released in September 2023, includes the **, ** Plus, ** Pro and Pro Max. Powered by the A16 bionic chip and running on iOS **, these models present improved displays, cameras, and functionalities. On the one hand, such features come, however, with hefty price tags, namely, an average of ***** U.S. dollars. On the other hand, they contribute to making Apple among the leading smartphone vendors worldwide, along with Samsung and Xiaomi. In the first quarter of 2024, Samsung shipped over ** million smartphones, while Apple recorded shipments of roughly ** million units. Success of Apple’s other products Apart from the iPhone, which is Apple’s most profitable product, Apple is also the inventor of other heavy-weight players in the consumer electronics market. The Mac computer and the iPad, like the iPhone, are both pioneers in their respective markets and have helped popularize the use of PCs and tablets. The iPad is especially successful, having remained as the largest vendor in the tablet market ever since its debut. The hottest new Apple gadget is undoubtedly the Apple Watch, which is a line of smartwatches that has fitness tracking capabilities and can be integrated via iOS with other Apple products and services. The Apple Watch has also been staying ahead of other smart watch vendors since its initial release and secures around ** percent of the market share as of the latest quarter.

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.

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.

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.

What’s Included

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.

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.

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.

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.

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

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.

Videos:4,349,945 (Users:2,060,573) 3,573,499 of videos are 3+ seconds

Gender: - Male 60% - Female 40%

Ethnicity: - Asian 9% - African Decent 13% - East Indian 3% - Latino Hispanic 28% - Caucasian 47%

Age Group: - 0-17 3% - 18-24 62% - 25-34 21% - 35-44 10% - 45-54 3% - 55+ 1%

Top Phone Models: - iPhone 6s 9% - iPhone XR 6% - iPhone 6 6% - iPhone 7 (US/CDMA) 6% - iPhone 11 5% - iPhone 8 (US/CDMA) 4%

Top Countries: - US 48.84% - GB 10.57% - CA 4.26% - AU 3.48% - FR 2.80% - SA 2.17%

Includes 474 000 users absent from the selfie photo set; the rest typically have one selfie and ~2 short voiced videos.

All videos are collected with the consent of users.

The global smartphone penetration in was forecast to continuously increase between 2024 and 2029 by in total 20.3 percentage points. After the fifteenth consecutive increasing year, the penetration is estimated to reach 74.98 percent and therefore a new peak in 2029. Notably, the smartphone penetration of was continuously increasing over the past years.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 North America and the Americas.

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. 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).

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.