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Counting 1
Guided Lesson
Counting 1
Teach preschoolers the skills necessary for learning how to count with this guided lesson. Kids learn one-to-one correspondence and how numerals relate to quantity. They also learn how to count out a given number of objects using both manipulatives and one-to-one correspondence. With scaffolded learning techniques that help kids build skills, as well as opportunites to practice these skills within a narrative, this lesson has counting covered.
Preschool
Math
Guided Lesson
Search Identifying Numbers 0-10 Educational Resources
If your student is still mastering her 123s, color by number is more than just a fun way for her to pass time. Completing these color by number and connect the dots worksheets will help your student identify numbers 1-10. Lesson plans will also help you teach this fundamental math skill. For more counting practice, check out our counting numbers 1-10 resources.
Easy as 123: Resources on Identifying Numbers 0-10
Counting to 10 is a child's first experience with math.
There are numerous tools in the Learning Library to help young learners identify these important numbers and commit this rudimentary skill to memory.
There are multiple teacher-created lesson plans that teach children number recognition. Five Busy Little Bees is a popular plan that is a close look at numbers 1-5. Students will learn how to identify and write 1, 2, 3, 4 and 5 with the help of colorful bumblebee cutouts. Leafy Numbers utilizes fresh fallen leaves from a tree to help students count all the way to 15. All of the lesson plans are complete with guided instruction. The plans include a group lesson as well as opportunities for independent working time. For students who grasp counting quickly, each plan includes enrichment options, too.
Catchy songs are always helpful when it comes to memorization. The library offers three song videos that kids enjoy singing along with. There is also a selection of professional online games including puzzles and a matching game that are both academic and entertaining.
There are many other options available for parents and teachers to help students begin their math journey on the right foot. The Learning Library resources on identifying numbers 0 through 10 make learning to count as easy as 123.
Preschool number recognition games 1-10 online + Worksheets
Free number recognition games online free. Recognition of numbers 1 to 10. Number identification games for kindergarten. Pre k number recognition kindergarten. Counting and number recognition online games. Recognising numbers to 10. kindergarten number recognition up to 3 games for preschoolers. Preschool number recognition games 1 10 online. Games for number recognition 1 10. Online number recognition games for preschoolers. Number recognition interactive games. number recognition up to 5 games for preschoolers.
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Click on the circles according to the given number.
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Preschool number recognition games online FREE
There are many fun ways to make math for our kids more interesting. We can start with basics activities such as number recognition flash cards and when the children are already familiar with numbers they can easily continue with these ´number recognition games for kindergarten´.
In this game kids color the circles according to the given number. Kids love the challenge of this exercise that teaches them recognize the numbers to 10 in the fun way.
After you fill in the circles, press the button "Check it". If the answer is OK, you will see "smiley face". If it is wrong, you will see the correct answer.
Number recognition up to 3 games
Click on the blue button 3 and play number recognition up to 3 games for preschoolers.
Number recognition up to 5 games for kindergarteners
Click on the blue button 5 and play interactive number recognition up to 5 games.
Number recognition games 1-10 online
Click on the blue button 10 and you can play online number recognition games 1-10.
Number recognition games 1-20
For recognizing numbers 1-20 play these counting games.
Play our interactive recognizing numbers games as many times as you want. Press the button "New game" and play another number recognition game online.
Kindergarten number recognition worksheets PDF
With these print-and-go number recognition worksheets PDFs, kids can practice number recognition, handwriting and fine motor practice as they familiarize themselves with the numbers 1-10.
Click the picture and download our free printable number recognition worksheets 1-10.
You can browse more free math worksheets for kindergarten here.
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Number recognition games 1-10 online
Identifying numbers can be a nice game for preschoolers. At this age, children begin to ask questions: What number is that?
There are many fun activities in which children learn to recognise numbers to 10. One of these activities is playing number recognition games online.
This number recognition game along with our
number matching games help kids to indetify numbers 1 to 10.
Number identification activity is brilliant for practising recognizing numbers and begin to learn how to count.
Kids can play our games teaching counting and number recognition online or you can download our free printable number recognition worksheets.
Recognition of numbers 1 to 10
Teaching number recognition may seem simple, but it is critical to a child’s early development. The concept of ‘counting’ is a vital process for a child. You can play our number recognition games 1-10 online on any device.
It is also related to other mathematical concepts such as matching, sorting and comparing numbers.
Number recognition up to 10 games for preschoolers
There are many ways children learn to recognize numbers 1-10. Of course, the number identification is best learned in everyday activities. Looking for an educational video teaching recognition of numbers 1-10? Then wath this one.
Number recognition online games
One of the most important early math skills is the recognizing numbers.
Many children will learn numbers simply by encountering them in the environment.
But most children need some real expertise and strategies to learn to recognize numbers.
These online recognizing numbers games help kids, their teachers and parents to master number recognition up to 10.
Enjoy our free number identification online games.
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Kindergarten Math Games
1st Grade Math Games
Number recognition. Practical guide. Part 1 / Sudo Null IT News
It all started trite - for a year now my company has been paying a monthly fee for a service that could find a region with license plates in a photo. This function is used to automatically sketch the number for some customers.
And one fine day, the Ministry of Internal Affairs of Ukraine opened access to the register of vehicles. Now it has become possible to check some information about the car by the license plate (make, model, year of manufacture, color, etc.
)! The boring routine of linear programming faded in front of a new super-task - to read numbers from the entire photo database and validate this data with those that the user specified. You know how it is, “eyes lit up” - the challenge was accepted, all other tasks became boring and monotonous for a while ... We set to work and got good results, which, in fact, we decided to share with the community. nine0005
For reference: about 100,000 photos are added to AUTO.RIA.com a day.
Data scientists have long known and are able to solve such problems, so dimabendera and I wrote this article specifically for programmers. If you are not afraid of the phrase “convolutional networks” and know how to write “Hello World” in python, you are welcome under the cat…
Who else will recognize
A year ago, I studied this market and it turned out that not so many services and software can work with exUSSR country numbers.
Below is a list of companies we have worked with:
There are many commercial systems with closed software, but we have not found a good open source implementation. In fact, this is very strange, since the open source tools that underlie the solution to this problem have long existed.
What tools are needed for license plate recognition
Finding objects in an image or video stream is a task from the field of computer vision, which is solved by different approaches, but most often with the help of the so-called convolutional neural networks. We need to find not only the area in the photo in which the desired object occurs, but also to separate all its points from other objects or the background. This kind of task is called "Instance Segmentation". The illustration below visualizes different types of computer vision tasks. nine0003
I'm not going to write a lot of theory about how the convolutional network works now, this information is enough on the net and reports on youtube.
Of the modern convolutional series architectures for segmentation problems, the following are often used: U-Net or Mask R-CNN. We chose Mask R-CNN.
The second tool we need is a text recognition library that can work with different languages and that can be easily customized to the specifics of the texts that we will recognize. Here the choice is not so great, the most advanced is tesseract from Google. nine0003
There are also a number of less "global" tools with which we will need to normalize the area with the license plate (bring it to a form in which text recognition will be possible). Usually opencv is used for such transformations.
It will also be possible to try to determine the country and type to which the found license plate belongs, in order to apply a refinement template specific to this country and this type of number in post-processing. For example, the Ukrainian license plate, starting from 2015, is designed in blue and yellow design and consists of the template “two letters four numbers two letters”.
nine0003
In addition, having statistics on the frequency of "meeting" in license plates of one or another combination of letters or numbers, you can improve the quality of post-processing in "disputable" situations. "
Nomeroff Net
From the title of the article it is clear that we have implemented everything and named the project Nomeroff Net. Now part of the code for this project is already working in production on the AUTO.RIA.com website. Of course, commercial analogues are still far away, everything works well only for Ukrainian numbers. In addition, acceptable speed is achieved only with GPU support from the tensorflow module! You can also try without a GPU, but not on Raspberry Pi :). nine0003
All materials for our project: tagged datasets and trained models, we have made publicly available with the permission of RIA.com under a Creative Commons CC BY 4.0 license
What we need
- Python3
- opencv-python version 3.
4 or higher - fresh Mask RCNN, tesseract
- through the pip3 package manager, you will need to install several python3 modules, they will be listed in a separate requirements.txt file
Dmitry and I have everything running on Fedora 28, I'm sure it can all be installed on any other Linux distribution. I would not want to turn this post into instructions for installing and configuring tensorflow, if you want to try and something doesn’t work, ask in the comments, I will definitely answer and prompt.
In order to speed up the installation, we plan to create a dockerfile - expect in the next updates of the project.
Nomeroff Net “Hello world”
Let's try to recognize something. We clone the repository with the code from github. We download the trained models for searching and classifying numbers into the models folder, we will slightly tweak the variables with the location of the folders.
UPD: This code is deprecated, it will only work in the 0.1.0 branch, see the latest examples here:
Everything can be recognized:
import os import sys import json import matplotlib.image as mpimg # change this property NOMEROFF_NET_DIR = "/var/www/nomeroff-net/" MASK_RCNN_DIR = "/var/www/Mask_RCNN/" MASK_RCNN_LOG_DIR = os.path.join(NOMEROFF_NET_DIR, "logs/") MASK_RCNN_MODEL_PATH = os.path.join(NOMEROFF_NET_DIR, "models/mask_rcnn_numberplate_0700.h5") REGION_MODEL_PATH = os.path.join(NOMEROFF_NET_DIR, "models/imagenet_vgg16_np_region_2019_1_18.h5") sys.path.append(NOMEROFF_NET_DIR) # Import license plate recognition tools. from NomeroffNet import filters, RectDetector, TextDetector, RegionDetector, Detector, textPostprocessing # Initialize npdetector with default configuration file. nnet = Detector(MASK_RCNN_DIR, MASK_RCNN_LOG_DIR) # Load weights in keras format. nnet.loadModel(MASK_RCNN_MODEL_PATH) # Initialize rect detector with default configuration file.rectDetector = RectDetector() # Initialize text detector. textDetector = TextDetector() # Initialize numberplate region detector. regionDetector = RegionDetector() regionDetector.load(REGION_MODEL_PATH) img_path = './examples/images/example1.jpeg' img = mpimg.imread(img_path) NP = nnet.detect([img]) # Generate image mask. cv_img_masks = filters.cv_img_mask(NP) for img_mask in cv_img_masks: # Detect points. points = rectDetector.detect(img_mask, fixRectangleAngle=1, outboundWidthOffset=3) # Split on zones zone = rectDetector.get_cv_zones(img, points) # find standard regionId = regionDetector.predict(zone) regionName = regionDetector.getLabels(regionId) # find text with postprocessing by numberplate region detector text = textDetector.detect(zone) text = textPostprocessing(text, regionName) print('Detected numberplate: "%s" in region [%s]'%(text,regionName)) # Detected numberplate: "AC4921CB" in region [eu-ua-2015]
Online demo
We sketched a simple demo for those who don't want to install all this and run it at home :).
Be indulgent and patient with the speed of the script.
If you need examples of Ukrainian numbers (to test the correction algorithms), take an example from this folder.
What's next
I understand that the topic is very niche and is unlikely to be of great interest to a wide range of programmers, in addition, the code and models are still quite “raw” in terms of recognition quality, speed, memory consumption, etc. But still there is hope that there will be enthusiasts who will be interested in training models for their needs, their country, who will help and suggest where there are problems and together with us will make the project no worse than commercial counterparts. nine0003
Known issues
- The project has no documentation, only basic code examples.
- The universal OCR tesseract is chosen as the recognition module and it can read a lot, but makes a lot of mistakes. In the case of recognition of Ukrainian license plates, a specialized correction system has been written there, which so far compensates for some of the errors, but there is a premonition that much better can be done here.
- "Square" numbers (license plates with a ratio of 1:2) are quite rare and we have just begun to deal with them, so the error will be greater with them. nine0047
- Sometimes, instead of a license plate, our model finds road signs with the name of a settlement, a dashboard inside the cabin, and other artifacts.
- If the quality of the number is poor or the resolution is low, the region of 4 points is not quite accurately determined
Announcement
If it will be interesting to someone, in the second part we are going to talk about how and with what to mark up your dataset and how to train your models that can work better for your content (your country, your photo size). We will also talk about how to create your own classifier, which, for example, will help determine if a number is drawn on a photo. nine0003
Some examples in Jupyter Notebook:
- Example of finding region masks with license plates
- Example of Finding and Converting Region Masks to Quad Polygons
- Example of license plate recognition with visualization
Useful links
- Nomeroff Net home page
- Nomeroff Net on GitHub
- Online demo Nomeroff Net nine0046 OpenALPR online demo
- UPD: Number recognition.
How we got 97% accuracy for Ukrainian numbers. Part 2 - UPD: License plate recognition. How to speed up everything
License plate recognition module - VIDEO EYE Moscow
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ITV Software "Intellect" - Radar connection
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nine0002 Code: 169619ITV Software "Intellect" - Recognition of vehicle numbers on board the camera
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ITV Software "Intellect" - Seenaptec license plate recognition (Slow-1)
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ITV Software "Intellect" - Seenaptec license plate recognition (Slow-4)
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Seenaptec license plate recognition in Slow mode (module processes video stream at up to 6 fps, recognizes license plates of vehicles moving at a speed of no more than 20 km /h). 4 channel license Show 9 more0003
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nine0002 Code: 183472ITV Software "Intellect" - Seenaptec license plate recognition (Slow-6)
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Seenaptec license plate recognition in Slow mode (module processes video stream at a speed of up to 6 fps, recognizes license plates of vehicles moving at a speed of no more than 20 km /h).
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ITV Software "Intellect" - Recognition of vehicle numbers Seenaptec (Slow-8)
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ITV Software "Intellect" - Seenaptec license plate recognition (Fast-1)
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Seenaptec license plate recognition in Fast mode (module processes video stream at up to 25 fps, recognizes license plates of vehicles moving at speeds up to 150 km / h).
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ITV Software "Intellect" - Seenaptec license plate recognition (Fast-2)
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ITV Software "Intellect" - Recognition of vehicle numbers Seenaptec (Fast-3)
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ITV Software "Intellect" - Seenaptec license plate recognition (Fast-4)
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Seenaptec license plate recognition in Fast mode (module processes video stream at up to 25 fps, recognizes license plates of vehicles moving at speeds up to 150 km / h). License for 4 video channels, Show 9 more0003
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nine0002 Code: 183478ITV Software "Intellect" - Seenaptec license plate recognition (Fast-6)
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Seenaptec license plate recognition in Fast mode (module processes video stream at up to 25 fps, recognizes license plates of vehicles moving at speeds up to 150 km / h).
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ITV Software "Intellect" - Recognition of vehicle numbers Seenaptec (Fast-8)
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ITV Software "Intellect" - Detector of stopped cars
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nine0002 Code: 183484ITV Software "Intellect" - Recognition of license plates on request
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Software "Intellect" - Recognition of license plates on request. The module allows, upon an event in the system, to capture an image from a given video camera and send it.00 economy
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License plate recognition module
The module uses a high-speed PTZ camera to capture vehicle numbers and generate an alarm when the vehicle is in one place longer than the specified threshold.
