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.

In 2022, smartphone vendors sold around 1.39 billion smartphones were sold worldwide, with this number forecast to drop to 1.34 billion in 2023.

Smartphone penetration rate still on the rise

Less than half of the world’s total population owned a smart device in 2016, but the smartphone penetration rate has continued climbing, reaching 78.05 percent in 2020. By 2025, it is forecast that almost 87 percent of all mobile users in the United States will own a smartphone, an increase from the 27 percent of mobile users in 2010.

Smartphone end user sales

In the United States alone, sales of smartphones were projected to be worth around 73 billion U.S. dollars in 2021, an increase from 18 billion dollars in 2010. Global sales of smartphones are expected to increase from 2020 to 2021 in every major region, as the market starts to recover from the initial impact of the coronavirus (COVID-19) pandemic.

The average time spent daily on a phone, not counting talking on the phone, has increased in recent years, reaching a total of * hours and ** minutes as of April 2022. This figure was expected to reach around * hours and ** minutes by 2024.

Smart phone price index (CPPI) by North American Product Classification System (NAPCS). The table includes annual data for the most recent reference period and the last four periods. Data are available from January 2015. The base period for the index is (2015=100).

As people increasingly communicate via asynchronous non-spoken modes on mobile devices, particularly text messaging (e.g., short message service (SMS)), longstanding assumptions and practices of social measurement via telephone survey interviewing are being challenged. This dataset contains 1,282 cases, 634 cases that completed an interview and 648 cases that were invited to participate, but did not start or complete an interview on their iPhone. Participants were randomly assigned to answer 32 questions from US social surveys via text messaging or speech, administered either by a human interviewer or by an automated interviewing system. 10 interviewers from the University of Michigan Survey Research Center administered voice and text interviews; automated systems launched parallel text and voice interviews at the same time as the human interviews were launched. The key question was how the interview mode affected the quality of the response data, in particular the precision of numerical answers (how many were not rounded), variation in answers to multiple questions with the same response scale (differentiation), and disclosure of socially undesirable information. Texting led to higher quality data--fewer rounded numerical answers, more differentiated answers to a battery of questions, and more disclosure of sensitive information--than voice interviews, both with human and automated interviewers. Text respondents also reported a strong preference for future interviews by text. The findings suggest that people interviewed on mobile devices at a time and place that is convenient for them, even when they are multitasking, can give more trustworthy and accurate answers than those in more traditional spoken interviews. The findings also suggest that answers from text interviews, when aggregated across a sample, can tell a different story about a population than answers from voice interviews, potentially altering the policy implications from a survey. Demographic variables include participants' gender, race, education level, and household income.

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 KU-BdSL refers to a Bengali sign language dataset, which includes three variants of the data. The variants are - (i) Uni-scale Sign Language Dataset (USLD), (ii) Multi-scale Sign Language Dataset (MSLD), and (iii) Annotated Multi-scale Sign Language Dataset (AMSLD). The dataset consists of images representing single-hand gestures for BdSL alphabets. Several smartphones are taken into account to capture images from 39 participants (30 males and 9 females). These 39 participants associated with the dataset creation have not offered any financial benefit. Each version includes 30 classes that resemble the 38 consonants ('shoroborno') of Bengali alphabets. There is a total of 1,500 images in jpg format in each variant. The images are captured on flat surfaces at different times of the day to vary the brightness and contrast. Class names are Unicode values corresponding to the Bengali alphabets for USLD and MSLD.

Folder Names: 2433 -> ‘Chandra Bindu’ 2434 -> ‘Anusshar’ 2435 -> ‘Bisharga’ 2453 -> ‘Ka’ 2454 -> ‘Kha’ 2455 -> ‘Ga’ 2456 -> ‘Gha’ 2457 -> ‘Uo’ 2458 -> ‘Ca’ 2459 -> ‘Cha’ 2460-2479 -> ‘Borgio Ja/Anta Ja’ 2461 -> ‘Jha’ 2462 -> ‘Yo’ 2463 -> ‘Ta’ 2464 -> ‘Tha’ 2465 -> ‘Da’ 2466 -> ‘Dha’ 2467-2472 -> ‘Murdha Na/Donto Na’ 2468-2510 -> ‘ta/Khanda ta’ 2469 -> ‘tha’ 2470 -> ‘da’ 2471 -> ‘dha’ 2474 -> ‘pa’ 2475 -> ‘fa’ 2476-2477 -> ‘Ba/Bha’ 2478 -> ‘Ma’ 2480-2524-2525 -> ‘Ba-y Ra/Da-y Ra/Dha-y Ra’ 2482 -> ‘La’ 2486-2488-2487 -> ‘Talobbo sha/Danta sa/Murdha Sha’ 2489 -> ‘Ha’

USLD: USLD has a unique size for all the images that is 512*512 pixels. The intended hand position is placed in the middle of the majority of cases in this dataset. MSLD: The raw images are stored in MSLD so that researchers can make changes to the dataset. The use of various smartphones yields us a wide variety of image sizes. AMSLD: AMSLD has multi-scale annotated data, which is suitable for tasks like localization and classification. From many annotation formats, the YOLO DarkNet annotation has been selected. Each image has an annotation text file containing five numbers separated by white space. The initial number is an integer, and the rest are floating numbers. The first number of the file indicates the class ID corresponding to the label of that image. Class IDs are mapped in a separate text file named 'obj.names'. The second and third values are the beginning normalized coordinates, while the fourth and fifth define the bounding box's normalized width and height.

This dataset is supported by Research and Innovation Center, Khulna University, Khulna-9208, Bangladesh and all the data from this dataset is free to download, modify, and use. The previous version (Version 1) of this dataset contains the oral permission of the volunteers, and the rest versions have written consent of the participants. Therefore, we encourage researchers to use these versions (Version 2 or Version 3 or Version 4) for research objective.

MasaFoundation/huberman_lab_Dr_Jonathan_Haidt_How_Smartphones_Social_Media_Impact_Mental_Health_the_Realisti dataset hosted on Hugging Face and contributed by the HF Datasets community

The study explores the direct and mediated impacts of customers’ perception of purchase budget (BGT) on purchase intention (PIT) through perceived quality (PPQ), perceived price (PPR), and perceived benefit (PB) in a cross-country setting to understand BGT’s role in predicting customer purchase intention in smartphone selling through international online shopping platforms. An online survey was conducted in Kenya, France, and the United States to gather data from 429 consumers who had recently purchased one or more smartphones through international online shopping platforms. SmartPLS-4 was used to test the hypotheses. Results for the entire sample showed a significantly positive mediating role of PPR and PPQ between BGT and PIT. However, the mediating roles of PPQ and PB were not significant in the samples from Kenya, France, and the United States. The results also showed that PPR plays a significant and positive mediating role between BGT and PIT in samples from Kenya, France, the United States, and overall. However, the direct relationships between BGT and PPQ, PPR, and PB are shown to be negatively significant.

The Japanese Kids Speech database (Upper Grade) contains the total recordings of 232 Japanese Kids speakers (104 males and 128 females), from 9 to 13 years’ old (fourth, fifth and sixth graders in elementary school), recorded in quiet rooms using smartphones. This database may be combined with the Japanese Kids Speech database (Lower Grade) also available in the ELRA Catalogue under reference ELRA-S0411.Number of speakers, utterances and duration, age are as follows :Number of speakers 232 (104 male/128 female)Number of utterances (average):385 utterances per speakerTotal number of utterances:89,454Age: from 9 to 13 years' oldTotal hours of data: 145.41018 sentences were used. Recordings were made through smartphones and audio data stored in .wav files as sequences of 16KHz Mono, 16 bits, Linear PCM.Database:・Audio data: WAV format, 16KHz, 16bit, mono (recorded with smartphone)・Recording scripts: TSV format(tab-delimited), UTF-8 (without BOM)・Transcription data: TSV format(tab-delimited), UTF-8 (without BOM)・Size: 16.2GBNumber of speakers per age:9 years' old: 56 (21 male, 35 female)10 years' old: 71 (30 male, 41 female)11 years' old: 65 (28 male, 37 female)12 years' old: 38 (24 male, 14 female)13 years' old: 2 (1 male, 1 female)Structure of database:├─ readme.txt├─ Japanese Kids Speech Database.pdfDescription document of the database├─ Transcription.tsvTranscription├─ scripts.tsvScript│└─ voices/directory of audio data ├─ high/directory of upper grade └─(speaker_ID/)directory of speaker ID (six digits) └─(audio_file)audio file (WAV format, 16KHz, 16bit, mono)File naming conventions of audio files are as follows:Field number | Contents | Description | Remarks0 | Language ID | “JA” (fixed) | Japanese1 | Speaker ID | Six digit | 5XXXXX2 | Script ID | HXXXX | XXXX: four digits3 | Age | Two digits4 | Gender | M: male, F: femaleFiled separation character is “_”.For example, if the audio file name is “JA_500002_H0001_10_F.wav, this file has the following meaning:JA: Language ID (Japanese)500002: speaker ID H0001: script ID 10: age (ten years old)F: gender (female)

Between 2015 and 2021, regardless of their age, the share of children owning a smartphone in the United States grew. During the 2021 survey, it was found that 31 percent of responding 8-year-olds owned a smartphone, up from only 11 percent in 2015.

ICDAR2015 competition on smartphone document capture and OCR (SmartDoc)

Challenge 2: MOBILE OCR COMPETITION

The goal of the competition is to extract the textual content from document images which are captured by mobile phones. The images are taken under varying conditions to provide a challenging input. The dataset was prepared for ICDAR2015-SmartDoc competition. For more details about the dataset please visit the competition's website:

https://sites.google.com/site/icdar15smartdoc/home

http://smartdoc.univ-lr.fr

You may also refer to the following paper for more details on the ICDAR2015-SmartDoc competition:

Jean-Christophe Burie, Joseph Chazalon, Mickaël Coustaty, Sébastien Eskenazi, Muhammad Muzzamil Luqman, Maroua Mehri, Nibal Nayef, Jean-Marc OGIER, Sophea Prum and Marçal Rusinol: “ICDAR2015 Competition on Smartphone Document Capture and OCR (SmartDoc)”, In 13th International Conference on Document Analysis and Recognition (ICDAR), 2015.

If you use this dataset, please send us a short email at

At Driver Technologies, we are dedicated to harnessing advanced technology to gather anonymized critical driving data through our innovative dash cam app, which operates seamlessly on end users' smartphones. Our Hard Braking Telematics Data offering is a key resource for understanding driver behavior and improving safety on the roads, making it an essential tool for various industries.

What Makes Our Data Unique? Our Hard Braking Data is distinguished by its real-time collection capabilities, utilizing the built-in accelerometer and gyroscope sensors of smartphones to capture telematics during driving. This data reflects instances of hard braking events, which are key indicators of aggressive driving behavior and potential risks on the road. Through our dataset, gain access to videos, processed through our computer vision model, of drivers hard braking and/or a telematics-only trip with an instance of a hard brake. By providing data on braking events, our dataset empowers clients to perform in-depth analysis.

How Is the Data Generally Sourced? The data is sourced directly from users who use our dash cam app. As users drive, our app monitors and records telematics data, ensuring that the information is both authentic and representative of real-world driving conditions.

Primary Use-Cases and Verticals Driver Behavior Analysis: Organizations can leverage our telematics data to analyze driving habits and identify trends in aggressive driving behavior. Improving Risk Assessment: Insurers can utilize our dataset to refine their risk assessment models. By understanding the frequency and context of hard braking events, they can better evaluate driver risk profiles, leading to more accurate premium pricing and improved underwriting processes.

Integration with Our Broader Data Offering The Hard Braking Data is a crucial component of our broader data offerings at Driver Technologies. It complements our extensive library of driving data collected from various vehicles and road users, creating a comprehensive data ecosystem that supports multiple verticals, including insurance, automotive technology, and smart city planning.

In summary, Driver Technologies' Hard Braking Data provides a unique opportunity for data buyers to access high-quality, actionable insights that drive innovation across mobility. By integrating our Hard Braking with other datasets, clients can gain a holistic view of transportation dynamics, enhancing their analytical capabilities and decision-making processes.

In the fourth quarter of 2024, Samsung shipped around 52 million smartphones, a decrease from the both the previous quarter and the same quarter of the previous year. Samsung’s sales consistently place the smartphone giant among the top three smartphone vendors in the world, alongside Xiaomi and Apple. Samsung smartphone sales – how many phones does Samsung sell? Global smartphone sales reached over 1.2 billion units during 2024. While the global smartphone market is led by Samsung and Apple, Xiaomi has gained ground following the decline of Huawei. Together, these three companies hold more than 50 percent of the global smartphone market share.

In 2023, the number of smartphone users in Singapore reached about *** million. This number has been increasing since 2020 and is expected to grow to over *** million by 2029. The use of smartphones and the internet Smartphones and internet use are growing hand in hand. In Singapore, internet penetration has been steadily increasing and is expected to rise even further in the following years, with the mobile internet penetration rate there among the highest in the world. Thanks to its well-developed telecommunications infrastructure, Singapore has one of the fastest mobile internet connection speeds in the region, becoming the first country in the world to achieve nationwide 5G coverage. Social media use The smartphone penetration in Singapore is high and social media are widely used. Meta’s platforms are the most popular, with WhatsApp and Facebook leading the way. Twitter has the largest advertising audience in the country. The growing use of social networks allows advertisers to reach a broad audience, resulting in revenues that are expected to reach ****** million U.S. dollars in 2022.

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.

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.