Principles and Practices of Text Recognition Technology in HarmonyOS Next

This article aims to deeply explore the text 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 Text Recognition Technology and Characteristics of HarmonyOS Next

(1) Detailed Explanation of the Technical Process

In the world of text recognition in HarmonyOS Next, its technical process is like a carefully choreographed dance, where every step is crucial.

First is the image preprocessing stage, which is like setting the stage for a grand performance. It mainly includes operations such as image grayscale conversion, noise reduction, binarization, and skew correction. For example, during the image grayscale conversion process, a color image is converted into a grayscale image, reducing the data volume while retaining the basic contour information of the text. The noise reduction operation removes noise interference in the image, such as salt-and-pepper noise and Gaussian noise, making the text clearer and more distinguishable. Binarization converts the pixel values of the image into black and white according to the set threshold, highlighting the contrast between the text and the background. Skew correction is aimed at images taken at an incorrect angle, adjusting the text area to the horizontal or vertical direction, preparing for subsequent character segmentation and recognition.

Next is the character segmentation link, which is similar to grouping the actors on the stage. For continuously written texts such as Chinese, character segmentation is a challenging task. In some cases, such as in printed documents, the text layout is relatively regular, and segmentation can be carried out according to the characteristics such as the spacing between characters and the distribution of strokes. However, in the case of handwritten texts or irregular layouts, character segmentation becomes much more complicated. For example, handwritten Chinese characters may have the phenomenon of connected strokes, and complex algorithms are required to determine the boundaries of characters, splitting the continuous text into individual characters for subsequent classification and recognition.

Finally is the classification and recognition stage, which is the core part of text recognition, just like the actors showing their talents on the stage. At this stage, a deep learning model (such as a convolutional neural network) is used to extract features and classify and recognize the segmented characters. The model is trained with a large amount of annotated data to learn the feature patterns of different characters, thereby determining what each character is. For example, for characters such as numbers "0-9" and letters "A-Z", the model can accurately recognize them according to their unique stroke structures and shape features.

(2) Analysis of the Support Situation of Text Recognition in HarmonyOS Next

HarmonyOS Next provides certain support capabilities in text recognition. In terms of image formats, it supports common formats such as JPEG, JPG, and PNG, which enables developers to conveniently process image files from various sources. In terms of language support, it covers multiple languages such as Simplified Chinese, English, Japanese, Korean, and Traditional Chinese, meeting the needs of text recognition in different languages. For example, in the office scenarios of multinational enterprises, document processing may involve multiple languages, and the text recognition ability of HarmonyOS Next can easily handle the text recognition tasks of these different languages. However, it should be noted that the document mentions that there is a lack of ability in handwritten font recognition, which also provides a direction for subsequent technical improvement and optimization.

(3) Comparison of the Advantages and Disadvantages of Different Text Recognition Technologies

1. Text Recognition Technology Based on Template Matching The advantage is that the algorithm is relatively simple, with a low computational complexity, and it is fast when dealing with some simple and standardized text recognition tasks. For example, for the recognition of numbers in some fixed-format tables, the method based on template matching can quickly compare the numbers with predefined templates to obtain the recognition result. However, its disadvantages are also obvious. It has poor adaptability to situations such as font changes, noise interference, and deformation. Once there are differences between the font, size, color, etc. of the text and the template, or there is noise in the image, the recognition accuracy will drop significantly. Moreover, for complex text structures (such as Chinese characters), a large number of templates are required to cover all possible situations, resulting in a large template library and high maintenance costs.
2. Text Recognition Technology Based on Deep Learning It has powerful learning ability and generalization ability, and can automatically learn the feature representation of text, and has a good recognition effect on various fonts, font sizes, handwritten texts, and texts with complex backgrounds. For example, when recognizing handwritten Chinese poems, the deep learning model can accurately recognize each Chinese character, even if there are situations such as connected strokes and scribbles. At the same time, with the increase of training data and the optimization of the model, its recognition accuracy can be continuously improved. However, the text recognition technology based on deep learning also has some shortcomings. It has high requirements for computing resources and requires powerful hardware acceleration such as GPUs or TPUs to train and run the model quickly. Moreover, a large amount of annotated data is required for model training. If the data is insufficient or the annotation is inaccurate, it will affect the performance of the model. In addition, the interpretability of the model is poor, and it is difficult to understand how the model makes recognition decisions.

II. Development of Text Recognition Functions and Application Examples

(1) Introduction to Recognition Methods and Code Examples (if applicable)

In HarmonyOS Next, some existing tools or libraries can be used to implement the text recognition function. Although the specific development library is not clearly mentioned in the document, we can assume that there is a similar text recognition library (similar to Tesseract OCR on other platforms). The following is a simple conceptual code example (assumed library and functions) to show the basic process of text recognition:

import { TextRecognitionLibrary } from '@ohos.textrecognition';

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

// Image preprocessing (assuming the library provides corresponding preprocessing functions)
let preprocessedImage = TextRecognitionLibrary.preprocessImage(image);

// Character segmentation (assuming the library provides character segmentation functions)
let segmentedCharacters = TextRecognitionLibrary.segmentCharacters(preprocessedImage);

// Classification and recognition
let recognizedText = '';
for (let character of segmentedCharacters) {
    let recognitionResult = TextRecognitionLibrary.recognizeCharacter(character);
    recognizedText += recognitionResult;
}

console.log('Recognition result:', recognizedText);

In this example, first, the image file is loaded, then the image is preprocessed, followed by character segmentation, and finally, each segmented character is classified and recognized, and the recognition results are combined into the final text. In actual development, detailed parameter settings and function calls need to be made according to the specific library and API used.

(2) Demonstration of the Processing of Different Types of Text Recognition Tasks

1. Recognition of Printed Text in Documents For the recognition of printed text in documents, since the text layout is relatively regular and the fonts and font sizes are relatively uniform, the recognition difficulty is relatively low. In the processing process, the image preprocessing stage can focus on noise reduction and binarization operations to improve the contrast between the text and the background. Character segmentation can be accurately carried out according to the layout rules of the document, such as line spacing and character spacing. In the classification and recognition stage, a deep learning model is trained on common printed fonts, which can quickly and accurately recognize the text content in the document. For example, when processing a company's financial statement document, the text recognition system can accurately recognize the numbers in the table, text titles, and other information, providing a basis for subsequent data processing and analysis.
2. Handwritten Font Recognition (Discussion on Improvement Directions) Although the document mentions that HarmonyOS Next has a lack of ability in handwritten font recognition, we can discuss some improvement directions. In the image preprocessing stage, according to the characteristics of handwritten text, such as uneven stroke thickness and writing tilt, more refined preprocessing algorithms can be adopted. For example, an adaptive binarization method can be used to adjust the threshold according to the local characteristics of the handwritten text to better highlight the strokes. For character segmentation, a more intelligent segmentation algorithm can be developed by combining the connected stroke features and writing habits of handwritten text. For example, by analyzing the direction and connection relationship of the strokes, the boundaries of the characters can be determined. In terms of classification and recognition, more handwritten font samples from different people can be collected for training to increase the adaptability of the model to various handwritten styles. At the same time, some special network structures or algorithms for handwritten text recognition can be introduced, such as a neural network based on the attention mechanism, so that the model can pay more attention to the key features in the handwritten text and improve the recognition accuracy.

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

1. Evaluation of Accuracy and Influencing Factors The accuracy of text recognition can be evaluated by comparing it with the manually annotated standard answers. For example, a certain number of documents or images containing different fonts, font sizes, layouts, and backgrounds are selected for testing, and the proportion of the number of correctly recognized characters to the total number of characters is calculated as the accuracy. There are many factors affecting the accuracy, and the quality of the picture is one of the key factors. If the image is blurry, the lighting is uneven, there are shadows, or there is noise interference, the features of the text will become blurred, increasing the recognition difficulty and reducing the accuracy. For example, in a document image taken under low-light conditions, the text may have shadows, making some strokes difficult to recognize, thus affecting the recognition result. The text layout also affects the accuracy. Situations such as too small line spacing, uneven character spacing, text tilt, or distortion will pose challenges to character segmentation and recognition. In addition, factors such as the font, font size, and language type of the text will also affect the accuracy. Some special fonts or rare characters may not be within the scope of the training data, resulting in recognition errors.
2. Evaluation of Performance and Influencing Factors Performance evaluation mainly focuses on the speed and resource occupancy of text recognition. The recognition speed can be evaluated by measuring the time spent from inputting the image to outputting the recognition result. The resource occupancy includes CPU usage, memory occupancy, etc. The factors affecting performance mainly include the size and resolution of the image, the complexity of the algorithm, and the performance of the hardware device. Larger-sized and higher-resolution images require more computing resources and time to process. The text recognition algorithm based on deep learning has a high computational complexity and also has high requirements for the performance of the hardware device. On low-end devices, problems such as slow recognition speed or even insufficient memory may occur. Therefore, in practical applications, it is necessary to select appropriate algorithms and parameter settings according to the performance of the device and the needs of the application scenario to balance the recognition accuracy and performance.

III. Optimization and Expansion Directions of Text Recognition Technology

(1) Proposed Optimization Methods

1. Improve Image Preprocessing Algorithms In the image preprocessing stage, more advanced noise reduction algorithms can be adopted, such as the noise reduction method based on wavelet transform, which can better preserve the detailed features of the text while removing noise. For skew correction, algorithms based on the Hough transform or image correction methods based on deep learning can be used to improve the accuracy and efficiency of correction. For example, when processing a document image with a large shooting angle, the image correction method based on deep learning can more accurately identify the skew angle of the text area and perform correction. At the same time, optimize the binarization algorithm, such as using a binarization method based on a local threshold, dynamically adjusting the threshold according to the brightness distribution of different areas of the image, making the binarized text clearer and reducing the situations of stroke breakage or adhesion.
2. Adopt More Advanced Deep Learning Models Explore the use of more advanced deep learning model architectures to improve the accuracy and performance of text recognition. For example, introduce the Transformer-based model architecture, which has achieved great success in the field of natural language processing. Applying it to the field of text recognition can better handle long sequences of character information and improve the ability to understand the context. At the same time, combine the attention mechanism to make the model pay more attention to the key parts of the text, such as the starting and ending positions of the strokes and the unique structure of the characters. In addition, use model compression technologies, such as pruning and quantization methods, to reduce the size of the model without significantly reducing the recognition accuracy, reduce the requirements for hardware resources, and improve the operation efficiency of the model on HarmonyOS Next devices.

(2) Discussion on Expanded Applications

1. Expansion of Applications in the Field of Intelligent Office In the intelligent office scenario, text recognition technology can achieve the automated processing of documents. For example, after scanning a paper document, the text in the document can be quickly converted into editable text through text recognition technology, facilitating document editing, archiving, and retrieval. Combined with natural language processing technology, it can also achieve intelligent analysis of the document content, such as extracting key information, semantic understanding, and classification. For example, when processing a corporate contract document, the text recognition system can recognize the key information such as the two parties to the contract, the contract amount, and the validity period, and automatically classify and archive it, improving office efficiency.
2. Expansion of Applications in the Field of Library Management In library management, text recognition technology can be used for the rapid entry and retrieval of books. By scanning the cover, table of contents, and some content of the book, information such as the book title, author, publisher, and ISBN number can be recognized, realizing the automated entry of book information. At the same time, for a large number of books in the library, text recognition technology can be used to index and generate abstracts of the book content, facilitating readers to quickly retrieve and query. For example, readers can enter keywords, and the system can quickly locate relevant books using text recognition and indexing technology and provide chapter abstracts containing the keywords to help readers quickly determine whether the book meets their needs.

(3) Summary of Experience and Lessons

1. The Importance of Data Annotation High-quality data annotation is the key to the successful training of text recognition models. During the annotation process, the accuracy and consistency of the annotation should be ensured. For some easily confused characters (such as the number "0" and the letter "O"), special symbols, etc., clear annotation specifications should be made. At the same time, the annotated samples should be diverse, covering different fonts, font sizes, writing styles, language types, etc., to improve the generalization ability of the model. For example, if only the text annotation of one font is included in the training data, the accuracy of the model may be very low when facing the text recognition task of other fonts.
2. Precautions in Model Training During the model training process, the training set, validation set, and test set should be reasonably divided. The validation set is used to evaluate the performance of the model during the training process, promptly discover problems such as overfitting or underfitting, and adjust the training parameters. The test set is used to finally evaluate the performance of the model to ensure the reliability of the model in practical applications. At the same time, pay attention to the selection of training parameters, such as the learning rate, the number of iterations, and the batch size. Selecting an appropriate learning rate can make the model converge to the optimal solution more quickly during the training process. If the learning rate is too large, the model may not converge; if the learning rate is too small, the training time will be too long. In addition, avoid the overfitting problem caused by excessive training. Regularization techniques (such as L1 and L2 regularization) and early stopping of training can be used to ensure that the model has good generalization ability. It is hoped that through the introduction of this article, everyone can have a deeper understanding of the text recognition technology in HarmonyOS Next and can better apply this technology in practical development, bringing more innovation and convenience to text processing-related applications. If you encounter other problems in the practice process, you are welcome to communicate and discuss together! Haha!