D Alphabet Images Download

Have not found anyone complaining about this yet, but:Have a flask app, normal static folder with js, css, and and image folder under it.The css, js go to the web page normally from the static folder, but the images:

Colourful letters with confetti tumble down a white background on this interactive and educational alphabet wallpaper. This engaging English alphabet wallpaper would make a terrific nursery room wallpaper perfect for toddlers as they get to grips with their very first phonetics. However, the fun and energetic design is suitable for kids of any age learning the English alphabet. Hang it in a bedroom, nursery or school to immerse children in the learning of letters!

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Most of the memory books dealing with this suggest the same subject to cover the alphabet. Christiane Stenger (A Sheep Falls out of the Tree) advocates cities (Atlanta, Birmingham, Cleveland, ...) or animals (Antelope, Baboon, Cougar,...). Jerry Lucas (Learning How to Learn) advocates several subjects, including sports team (Astros, Braves, Cowboys,...).

The words were written by the ancient Canaanites, a Near Eastern peoples who developed the earliest alphabet, an ancestor of our modern-day Latin letters. There were earlier forms of writing, with Mesopotamia cuneiform and Egyptian hieroglyphics emerging around 3,200 B.C.E., but those were pictorially based systems with hundreds of letters.

The development of the Canaanite alphabet, with its comparatively small number of letters that corresponded to basic spoken sounds, or phonemes, was a groundbreaking moment in human history. Because each letter matched one sound, reading and writing became easier to master, simplifying written communication for the masses in much the same way as the printing press or the internet did many generations later.

This PDF file includes one coloring page for each of the sign language letters and a picture and word for each. This is a great way to help kids learn the ASL alphabet and is a fun activity for your ASL sessions. You can even hand out one letter to each student in your class to color and hang them up on the wall for reference!

We are honestly completely fascinated by the manual alphabets from around the world. They vary greatly. The alphabet used in Australia, Britain, and New Zealand is the same and uses two hands instead of one. We highly recommend learning this alphabet simply for the fun factor.

7. Sort by beginning sound. When you print the alphabet cards (available as a separate download), you can use them as headers for the columns. Give your child just two sets of cards, for two different letters. Help him identify the beginning sound and sort.

Multiple attention-based models that recognize objects via a sequence of glimpses have reported results on handwritten numeral recognition. However, no attention-tracking data for handwritten numeral or alphabet recognition is available. Availability of such data would allow attention-based models to be evaluated in comparison to human performance. We collect mouse-click attention tracking data from 382 participants trying to recognize handwritten numerals and alphabets (upper and lowercase) from images via sequential sampling. Images from benchmark datasets are presented as stimuli. The collected dataset, called AttentionMNIST, consists of a sequence of sample (mouse click) locations, predicted class label(s) at each sampling, and the duration of each sampling. On average, our participants observe only 12.8% of an image for recognition. We propose a baseline model to predict the location and the class(es) a participant will select at the next sampling. When exposed to the same stimuli and experimental conditions as our participants, a highly-cited attention-based reinforcement model falls short of human efficiency.

We fill in that gap by collecting a dataset from adult participants trying to recognize handwritten numerals and alphabets from images via sequential sampling. Unlike eye-movement attention tracking (emAT), a participant clicks the location in the image that he wants to see (a form of mouse-click attention tracking (mcAT)). Immediately after that, he selects the class(es) that he predicts the object might belong to based on his observations so far. Thus, at each sampling episode, our data consists of the image location selected, class label(s) predicted, and time taken since last episode by the participant. After each image, the participant receives a reward based on his performance (accuracy and efficiency).

We collect an mcAT dataset, called AttentionMNIST, using MTurk from 382 participants, rewarded for accurately and efficiently recognizing handwritten numerals and alphabets (upper and lowercase) from images via sequential sampling. Images from benchmark datasets (MNIST, EMNIST) are presented as stimuli. On average, 169.1 responses per numeral/alphabet class are recorded. Using this dataset, we show the following:

On average, participants require 4.2, 4.7 and 4.9 samples to recognize a numeral, uppercase and lowercase alphabet, which correspond to only 11.3%, 13.4% and 13.7% of image area respectively. Classification accuracy increases with number of samples.

When exposed to the same stimuli and conditions as our participants, a highly-cited reinforcement-based recurrent attention model (RAM)3 requires 3.7, 8.5, 7.6 samples to recognize a numeral, uppercase and lowercase alphabet, which correspond to 8.9%, 21.0%, 18.7% of image area respectively. Other attention-based reinforcement models (e.g.,1,2,4,5,7,14) can be similarly evaluated in comparison to human performance.

Different kinds of stimuli have been used in mcAT studies, such as images of animate and inanimate objects10, images of natural scenes12,13, static webpages13, search page layouts16, and two lists of alphanumeric strings for visual comparison17. However, mcAT has not been used for handwritten numeral/alphabet classification tasks or evaluation of attention-based classification models.

EMNIST22 dataset consists of \(145,\!600\) images (\(28\!\times \!28\) pixels) of handwritten English alphabets in uppercase and lowercase, forming a balanced class. All images are labeled with one of 26 classes \(\{a,b,\ldots ,z\}\). However, uppercase or lowercase label is not associated with any image.

From each category, we select 15 well-formed numerals from MNIST and 15 well-formed alphabets each from EMNIST uppercase and EMNIST lowercase datasets. A well-formed numeral or alphabet is one that is similar to the norm of its class. Thus, we present stimuli from a set of \(15(10+26+26) = 930\) unique images, with 15 images belonging to each of the 62 classes.

Label well-formed EMNIST images as uppercase or lowercase. For each alphabet class, a well-formed alphabet from both uppercase and lowercase images is manually selected and labeled. The cosine similarity of all images belonging to that class with the two labeled images is computed. The images that are above the cosine similarity threshold (empirically chosen as 0.8) are assigned the uppercase or lowercase label.

Compute the mean of the images belonging to each class. The mean image of a class constitutes its norm. An image is eligible to be a stimulus if its cosine similarity with the mean image of its class is greater than an empirically-determined threshold (0.7 for MNIST, 0.75 for EMNIST).

Each image, originally \(28\!\times \!28\) pixels, is reduced to \(27\!\times \!25\) by removing the pixels near the boundaries as they have no intensity variation. The mean of these 15 images is computed for each of the 62 classes. We denote these mean images as \(I_1, I_2, \ldots , I_n\) for n classes in each dataset.

A total of 382 distinct adult individuals participated in our study. No selection criteria were used. A participant could respond to multiple images. For each of the 62 classes, an average of 169.1 responses were recorded.

The MTurk interface for our visual task is shown in Fig. 1. A canvas of size \(270 \!\times \!250\) displays a low-intensity background image at all time. The background and stimulus images are upsampled ten times to \(270\!\times \!250\). The center of the canvas is aligned with the center of the images.

Background Initially, the background is the mean of all images in the dataset from which the stimulus is drawn. After the first episode, the background is the mean of all images from the set of classes selected by the participant in the last episode. In the real world, the context for location, size and orientation of a numeral or alphabet is obtained from the writing in its neighborhood, which is missing here. When our experiments were conducted with a blank background, the participants often sampled locations of the image that do not contain any part of the object. This behavior was contained by presenting the mean image of the selected class(es) in a low-intensity background and reducing the size of all MNIST and EMNIST images from \(28\!\times \!28\) pixels to \(27\!\times \!25\).

Recognize the numeral/alphabet from all the samples observed so far. The participant can select multiple classes and will have to choose at least one class from the list of classes shown below the canvas.

In order to infer the class accurately and quickly, the participant will have to choose the locations judiciously given his observations till the current episode. There is no time limit for an episode. However, we limit the total time for T episodes of an image to six minutes. We choose \(T=12\) as highly-cited works on attention-based handwriting recognition or generation have used fewer than 12 glimpses (e.g., RAM3 could recognize MNIST numerals within 7 glimpses, DRAW23 could generate MNIST numerals within 11 glimpses), and humans can recognize handwritten numerals and alphabets in much fewer than 12 glimpses.

In this section, we illustrate the utility of the collected data by: (4.1) providing a baseline model for predicting the behavior of a participant, and (4.2) showing how an existing attention-based reinforcement model can be compared to human numeral/alphabet recognition performance. e24fc04721