AI Image Recognition Technology and Scenario Applications in HarmonyOS Next

This article aims to deeply explore the AI image recognition technology in the Huawei HarmonyOS Next system (up to API 12 as of now), and summarize it based on practical development practices. It mainly serves as a vehicle for technical sharing and communication. There may be mistakes and omissions. Colleagues are welcome to put forward valuable opinions and questions so that we can make progress together. This article is original content, and any form of reprint must indicate the source and the original author.

I. Foundation of AI Image Recognition Technology and Support from HarmonyOS Next

(1) Introduction to Main Technical Principles

1. Principle of Contextual Text Recognition In the AI image recognition system of HarmonyOS Next, contextual text recognition is a key technology. Its principle is mainly based on convolutional neural networks (CNNs) and recurrent neural networks (RNNs) or their variants (such as LSTM, GRU) in deep learning. First, the CNN extracts features from the input image. It can automatically learn the feature representations of different regions in the image, such as the stroke structure and texture of the text. Then, the RNN or its variant performs sequential modeling on the extracted features because text usually exists in a sequential form in the image (from left to right or from top to bottom). In this way, the model can understand the semantics and structure of the text, thereby accurately recognizing the text content in the image. For example, when recognizing an image containing the product name and price, the CNN extracts the features of the text region, and the RNN recognizes the specific product name and price digits according to these features and combines them in the correct order.
2. Principle of Object Segmentation Technology The object segmentation technology aims to separate the main object in the image from the background. Its core principle is to use a deep learning model to classify each pixel in the image and determine whether it belongs to the object or the background. A common method is to adopt the fully convolutional neural network (FCN) architecture, which can accept images of any size as input and output pixel-level classification results with the same size as the input image. During the training process, through a large amount of image data labeled with objects and backgrounds, the model learns the feature differences between objects and backgrounds in terms of color, texture, shape, etc., so as to accurately segment the objects. For example, in a portrait photo, the object segmentation model can accurately separate the human subject from the complex background (such as scenery, buildings, etc.), providing a basis for subsequent image processing (such as background replacement, portrait matting, etc.).
3. Principle of Image Recognition Search Technology The principle of image recognition search technology is based on the similarity matching of image features. First, feature extraction is performed on the input query image, and technologies such as convolutional neural networks in deep learning are also used. The extracted feature vectors can represent the key information of the image, such as the theme of the image, color distribution, texture features, etc. Then, these feature vectors are compared with the feature vectors in the pre-established image database, and the similarity is calculated. Commonly used similarity calculation methods include cosine similarity, Euclidean distance, etc. By comparing the similarity, the images most similar to the query image are found from the database and the search results are returned. For example, in an image search engine, when a user uploads a landscape image, the system extracts its features and searches for similar landscape images in the database, providing relevant image resources for the user.

(2) Analysis of Support from HarmonyOS Next

HarmonyOS Next provides certain support capabilities for AI image recognition technology. In terms of image specifications, it supports images with a minimum specification of 100*100 resolution, which provides a basis for processing images of various sizes. In terms of text languages, it supports multiple languages such as Simplified Chinese, Traditional Chinese, English, Uyghur, Tibetan, etc., meeting the application requirements in different language environments. For example, in a multilingual application scenario, whether it is a Chinese poster, an English book cover, or a Uyghur promotional flyer, the AI image recognition technology of HarmonyOS Next is capable of recognizing and processing it. This multilingual support enables the AI image recognition technology to play a role in various application scenarios worldwide, such as image recognition during international travel and multilingual document processing.

(3) Comparison of Performance and Accuracy of Different AI Image Recognition Technologies

1. Image Recognition Technology Based on Traditional Image Processing Methods Image recognition technology based on traditional image processing methods has certain advantages in some simple tasks. For example, when processing images with regular shapes and simple backgrounds, traditional methods such as template matching and edge detection can quickly identify the target object. Its computational complexity is relatively low, and the requirements for hardware resources are not high, so it can still run on devices with limited resources. However, when facing complex scenes, diverse image contents, and high-resolution images, its performance and accuracy will be greatly limited. For example, when recognizing an image containing multiple objects, a complex background, and blurry text, traditional methods may not be able to accurately extract all the information, and misrecognition or missed recognition is likely to occur.
2. AI Image Recognition Technology Based on Deep Learning AI image recognition technology based on deep learning has significant advantages in terms of performance and accuracy. It can automatically learn the complex feature representations in images and has strong adaptability to various scenes and different types of images. Whether it is contextual text recognition, object segmentation, or image recognition search tasks, deep learning models can achieve high accuracy. For example, in contextual text recognition under complex backgrounds, deep learning models can accurately recognize texts with different fonts, sizes, colors, and angles; in object segmentation tasks, they can finely segment object objects of various shapes and postures. However, deep learning-based technologies have high requirements for hardware resources and require powerful computing resources such as CPUs, GPUs, or NPUs to support the training and inference processes of the models. On devices with insufficient resources, it may run slowly or even be unable to run.

II. Implementation of AI Image Recognition Functions and Demonstration of Application Scenarios

(1) Explanation of Function Implementation Methods and Code Examples (if applicable)

Although the specific AI image recognition development library is not clearly mentioned in the document, we can assume that there is a similar function library (similar to TensorFlow Lite or OpenCV on other platforms). The following is a simplified conceptual code example to show the basic process of contextual text recognition (assuming libraries and functions):

import { AIImageRecognitionLibrary } from '@ohos.aiimagerecognition';

// Load the image (assuming the image file path has been obtained)
let imagePath ='scene_text.jpg';
let image = AIImageRecognitionLibrary.loadImage(imagePath);

// Perform contextual text recognition
let recognitionResult = AIImageRecognitionLibrary.recognizeSceneText(image);

console.log('Recognition result:', recognitionResult.text);

In this example, first, the image is loaded, then the contextual text recognition function is called to recognize the image, and finally, the recognition result is output. In actual development, detailed parameter settings and function calls need to be made according to the specific library and API used, including model selection, recognition threshold setting, etc., to achieve accurate AI image recognition functions.

(2) Demonstration of Different Scenario Applications

1. Application Scenario of Smart Photo Album AI image recognition technology plays an important role in the application of smart photo albums. When users take photos or import images into the photo album, AI image recognition can automatically perform contextual text recognition on the images. For example, it can recognize the location name, shooting time (if there is relevant text information in the photo), and the name of the person (if the person is marked or recognized in the photo) in the photo, and classify and label the photos according to this information. At the same time, using the object segmentation technology, the smart photo album can automatically separate the human subject from the background, providing users with functions such as one-click matting, background blurring, or background replacement, facilitating users to perform creative photo editing. For example, users can easily replace the background of their photos with beautiful scenery or interesting patterns without using professional image processing software.
2. Application Scenario of Image Editing In the image editing application, the object segmentation technology is a very practical function. Users can use the object segmentation function to quickly select the main object in the image, and then perform separate editing operations on the object, such as adjusting the color, contrast, saturation, etc., without affecting the background. For example, when editing a pet photo, the user can first use object segmentation to separate the pet from the background, and then adjust only the pet's fur color to make it more vivid, while the background remains unchanged. In addition, the image recognition search function can also be integrated into the image editing application. Users can search for similar image materials by uploading an image or selecting an image from the photo album, which can be used for creative composition or to obtain inspiration. For example, when designing a poster, a designer can find image elements related to the theme through image recognition search and then integrate them into their design.

(3) Evaluation of Performance and Effects and Analysis of Influencing Factors

1. Performance Evaluation Indicators and Methods The performance of the AI image recognition function is mainly evaluated by the recognition speed and resource occupation. The recognition speed can be measured by the time spent from inputting the image to outputting the recognition result. In actual testing, images of different sizes and different content complexities can be used for testing, and the average value can be taken as the indicator of the recognition speed. The resource occupation includes CPU usage, memory occupation, etc., which can be monitored through the performance monitoring tools provided by the system. For example, when testing the contextual text recognition function, record the average CPU usage and the peak memory occupation during the recognition of a batch of images to evaluate the resource consumption of this function on the device.
2. Effect Evaluation Indicators and Methods Effect evaluation mainly focuses on the accuracy and integrity of AI image recognition. For contextual text recognition, the accuracy can be measured by comparing it with the manually annotated text and calculating the proportion of the number of correctly recognized characters to the total number of characters. Integrity considers whether all the important text information in the image has been recognized. For example, when recognizing a product image containing information such as the product name, specifications, and price, accurately recognizing all this information without omission indicates good integrity. For object segmentation, the effect can be evaluated by the segmentation accuracy (such as the accuracy of the object edge, whether there are redundant or missing parts) and the recall rate (whether all the object objects have been correctly segmented). It can be judged by visual inspection and comparison with the manual segmentation results. For image recognition search, the effect can be evaluated by the relevance of the search results and the accuracy of the ranking, that is, whether the searched images are truly relevant to the query image and whether the relevant images are ranked at the front.
3. Analysis of Influencing Factors The complexity of the image content has a significant impact on the results of AI image recognition. In contextual text recognition, factors such as a complex background, diverse fonts, different text arrangement directions, and the contrast between the text and the background will all affect the recognition accuracy. For example, in a poster image containing artistic fonts, handwritten fonts, and printed fonts, and with a complex pattern as the background, the recognition difficulty will be greatly increased. In object segmentation, the shape, size, posture of the object object, and the degree of integration with the background will all affect the segmentation effect. For example, when the object object has a similar color to the background or the object is partially occluded, the difficulty of segmentation will increase. The image resolution also affects the performance and effect. Although high-resolution images may contain more information, they will increase the amount of calculation, resulting in a slower recognition speed, and may also increase the risk of misrecognition because there may be more detail interference at high resolutions. For example, when processing ultra-high-resolution landscape photos, the AI image recognition technology may take longer to process, and errors may occur when recognizing small objects or texts in the image.

III. Optimization and Expansion Directions of AI Image Recognition Technology

(1) Proposed Optimization Methods

1. Model Optimization and Compression To improve the performance of AI image recognition technology on HarmonyOS Next devices, deep learning models can be optimized and compressed. Adopt model quantization technology to convert the parameters in the model from high-precision data types (such as 32-bit floating-point numbers) to low-precision data types (such as 8-bit integers), which can reduce the storage size and calculation amount of the model and maintain the accuracy of the model to a certain extent. For example, in the contextual text recognition model, through quantization, the model can run faster and occupy less memory resources without significantly reducing the recognition accuracy. In addition, perform pruning operations on the model to remove unimportant connections or neurons and further reduce the size of the model. During the pruning process, according to the structure of the model and the task requirements, select an appropriate pruning strategy to avoid performance degradation caused by excessive pruning. For example, for the object segmentation model, according to the analysis of the importance of object and background features, cut off the connections that have little impact on the segmentation result to improve the operation efficiency of the model.
2. Data Augmentation and Improvement of Preprocessing The generalization ability and accuracy of the AI image recognition model can be improved through data augmentation technology. Randomly transform the training data, such as operations like rotation, flipping, scaling, cropping, adding noise, etc., to increase the diversity of the data. For example, in the training data of contextual text recognition, randomly rotate and scale the images containing text so that the model can learn the text features at different angles and sizes and improve the recognition ability of texts in various postures in practical applications. In terms of data preprocessing, improve the image normalization method. According to the content of the image and the task requirements, select more appropriate normalization parameters to make the data have better numerical stability during the model training and inference processes. For example, for the object segmentation task, according to the color distribution characteristics of the object and the background in the image, adopt an adaptive normalization method to improve the accuracy of object segmentation.

(2) Discussion on Expansion Application Directions

1. Integrated Application with Smart Security Systems AI image recognition technology can be deeply integrated with the smart security system of HarmonyOS Next. In the analysis of surveillance videos, the contextual text recognition technology can be used to recognize text information such as license plate numbers, store signs, and warning signs in the video images, providing more clues and data support for security surveillance. For example, in traffic monitoring, automatically recognizing license plate numbers can be used for capturing traffic violations and vehicle tracking. The object segmentation technology can be used to detect and track people or objects in the video. When abnormal behaviors (such as people entering restricted areas, objects being stolen, etc.) are detected, an alarm is issued in a timely manner. The image recognition search technology can be used to quickly retrieve historical video clips or images related to the surveillance scene, assisting security personnel in event investigation and analysis. For example, after a theft case occurs, by uploading the images of the items at the scene, the image recognition search function is used to search for relevant clues in the surveillance video database, improving the intelligence level of the security system and the efficiency of solving cases.
2. Expansion of Applications in the Field of Smart Education In the field of smart education, AI image recognition technology also has broad application prospects. In electronic textbooks and learning materials, contextual text recognition can help students quickly find and understand important knowledge points, such as recognizing text information such as formulas, chart titles, and key concepts in textbooks, and providing relevant explanations and extended materials. Teachers can use the object segmentation technology to grade students' homework and test papers. For example, separate the students' handwritten answers from the background of the test paper, and then use OCR technology to recognize the answers and grade them automatically. The image recognition search technology can be used for the recommendation and sharing of educational resources. Teachers and students can search for relevant teaching cases, courseware, experimental guidance, and other resources by uploading images (such as teaching scene images, experimental equipment images, etc.), enriching the teaching content and learning methods. In addition, in smart classrooms, the AI image recognition technology can be used to analyze students' classroom behaviors. For example, by recognizing students' facial expressions, postures, and other information, judge students' learning status and the degree of concentration, providing teaching feedback and personalized teaching suggestions for teachers.

(3) Experience Summary and Precautions

1. Experience in Model Training and Optimization During the training process of the AI image recognition model, the quality and diversity of the data are crucial. Collecting high-quality and diverse training data can improve the generalization ability and accuracy of the model. Ensure that the training data covers various scenarios, different types of images, and possible change situations. For example, in the training of contextual text recognition, collect text images with different fonts, sizes, colors, and backgrounds, including text in natural scenes (such as street signs, product labels, etc.) and artificially synthesized text images. At the same time, reasonably divide the training set, validation set, and test set, and use the validation set to monitor the training process of the model, adjust the training parameters (such as learning rate, number of iterations, etc.) in a timely manner, and avoid overfitting or underfitting. In terms of model optimization, according to the performance of the device and application requirements, select appropriate optimization technologies and parameter settings. For example, on devices with limited resources, give priority to model quantization and pruning technologies to reduce resource consumption while ensuring certain performance.
2. Precautions for Application Integration When integrating AI image recognition technology into specific applications, pay attention to its integration with the overall architecture of the application and the user experience. Ensure that the calling method of the AI image recognition function is simple and convenient and does not affect the original operation process of the application. For example, in the smart photo album application, the AI image recognition function can run automatically in the background. When the user opens the photo album, the recognition result is already ready, without causing additional waiting time for the user. At the same time, consider the security of data transmission and storage, especially when dealing with image data involving user privacy. Encrypt the transmission of image data and store it securely on the device to avoid the risk of data leakage. In addition, pay attention to the performance optimization of the application to avoid a decline in the overall performance of the application due to the addition of the AI image recognition function. For example, reasonably control the computational resource occupation of AI image recognition and adopt asynchronous processing and other methods to ensure that the application remains smooth when running the AI image recognition function. It is hoped that through the introduction of this article, everyone can have a deeper understanding of the AI image recognition technology in HarmonyOS Next and can better apply this technology in practical development, providing more possibilities for the innovation and development of smart applications. If you encounter other problems in the practice process, you are welcome to communicate and discuss together! Haha!