Practical Application of Text Recognition and Document Scanning in the Smart Office System of HarmonyOS Next

This article aims to deeply explore the practical application of text recognition and document scanning technologies in building a smart office system based on the Huawei HarmonyOS Next system (up to API 12 as of now), and summarize it based on practical development experience. 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. Requirements and Architecture Design of the Smart Office System

(1) In-depth Analysis of Functional Requirements

1. Requirements for Document Content Extraction In a smart office environment, quickly and accurately extracting document content is a key requirement. Whether it is a paper document or an electronic document (such as a document in PDF or image format), the text information in it needs to be efficiently extracted and converted into an editable text format. For example, when an enterprise processes a large number of contract documents, it needs to extract key clauses, amounts, the parties involved, and other information in the contracts for data analysis, archiving, and subsequent business processing. This requires the text recognition technology to be able to accurately recognize text in common fonts, and also handle some special fonts, handwritten text (although the document mentions that HarmonyOS Next has some deficiencies in handwritten font recognition, a small number of handwritten annotations may still be encountered in actual office work), and text in different languages (such as multilingual documents related to cross-border businesses).
2. Requirements for Electronic Document Generation Based on document content extraction, the smart office system needs to be able to generate high-quality electronic documents. For scanned paper documents, they should be converted into clear and standardized electronic documents, such as PDF or Word format, and the original layout structure of the document should be maintained. For example, after scanning a paper report and converting it into an electronic document, the generated PDF file should be consistent with the original paper document in terms of page layout, text formatting, chart position, etc., making it convenient for users to view, edit, and share. At the same time, the electronic document generation process should also support the addition of metadata, such as document title, author, date, and other information, to facilitate document management and retrieval.

(2) Architecture Design Based on HarmonyOS Next

1. Considerations for Hardware Selection To achieve efficient text recognition and document scanning functions, hardware selection is crucial. For document scanning devices, choosing devices with high-resolution cameras and autofocus functions is key. A high-resolution camera can capture clear details of the document, ensuring the clarity of text and images, which is conducive to subsequent recognition and processing. For example, when scanning a document containing small fonts or fine charts, a high-resolution camera can accurately obtain this information and avoid information loss. The autofocus function ensures that a clear image can be quickly obtained when shooting the document at different distances and angles, improving the scanning efficiency. At the same time, considering the portability and ease of use of the device, such as choosing a handheld scanner or a smartphone or tablet that supports the document scanning function, it is convenient for users to use in different scenarios. In terms of processing devices, choose HarmonyOS Next devices with relatively strong performance, such as devices equipped with a multi-core CPU, sufficient memory, and large-capacity storage. The text recognition and document scanning processing processes involve a large amount of image data processing and calculation tasks. A powerful CPU can quickly process these tasks and reduce the user's waiting time. Sufficient memory is used to store the image data being processed and model parameters to avoid slow program operation or crashes due to insufficient memory. Large-capacity storage is used to save scanned document images, recognition results, and generated electronic documents and other data.
2. Software Hierarchical Architecture Design
   • Data Acquisition Layer: Responsible for collecting document data from various sources, including using a scanner or camera to shoot paper documents and receiving electronic documents (such as email attachments, cloud storage downloads, etc.). At this layer, it is necessary to ensure the stability and compatibility of data acquisition, and support multiple document formats and acquisition methods. For example, for camera acquisition, a user-friendly shooting interface should be provided to guide users to obtain clear and complete document images; for electronic document reception, it should be able to correctly parse documents in different formats, such as PDF, JPEG, PNG, etc.
   • Recognition Processing Layer: It is the core layer of the system and integrates the core technologies of text recognition and document scanning. Utilize the text recognition capabilities provided by HarmonyOS Next to extract text from the collected document data. In this process, combined with data preprocessing technologies, such as image grayscale, noise reduction, binarization, skew correction, and other operations, the accuracy of text recognition is improved. For document scanning, call the relevant interfaces to complete the processing of document images, including edge detection, cropping, image enhancement, and other operations to generate high-quality scanned copies. At the same time, at this layer, the data interaction and collaborative processing between text recognition and document scanning are realized. For example, extract the text area from the scanned copy for recognition, and associate and integrate the recognition result with the scanned image.
   • Storage Layer: Used to store the original data of documents, recognition results, generated electronic documents, and configuration information during the system operation. Select an appropriate storage method, such as local file system storage, database storage (for structured data, such as document metadata, structured information of recognition results, etc.) or cloud storage (convenient for data backup, sharing, and cross-device access). During the storage process, ensure the security and integrity of the data, and use encryption technology to protect sensitive data, such as encrypting and storing document content containing business secrets.

(3) Elaboration on the Technical Collaborative Working Mechanism

In the architecture of the smart office system, text recognition and document scanning technologies work closely together. After the data acquisition layer obtains the document data, it passes it to the recognition processing layer. If it is a scanned image of a paper document, it first enters the document scanning processing flow, and a clear scanned copy is generated through image processing operations. Then, the text area is extracted from the scanned copy and input into the text recognition module for recognition. The text recognition module uses the text recognition technology of HarmonyOS Next, combined with the preprocessed image data, to accurately extract the text information. The recognition result is associated with the scanned copy. For example, the recognized text is overlaid on the scanned image in the form of a text layer, which is convenient for users to view and compare. At the same time, the recognition result can also be further structured. For example, according to the document type (such as contract, report, etc.), key information is extracted to form structured data, which is stored in the storage layer. For electronic documents, they directly enter the text recognition process. The recognized text information can be used for operations such as document content search, editing, and reformatting to generate new electronic documents or update the metadata of the original document, realizing the collaborative work of text recognition and document scanning technologies in the entire smart office system and improving office efficiency and the intelligent level of document management.

II. Implementation of Core Functions and Technical Integration

(1) Implementation and Optimization of the Text Recognition Function

1. Implementation Process Using HarmonyOS Next Technology Although the specific text recognition development library is not clearly mentioned in the document, we can assume that there is a similar function library (similar to Tesseract OCR on other platforms). The following is a simplified conceptual code example to show the basic process of text recognition using relevant technologies of HarmonyOS Next (assuming libraries and functions):

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

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

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

// Text recognition
let recognitionResult = TextRecognitionLibrary.recognizeText(preprocessedImage);

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

In this example, first, the document image is loaded, then the image is preprocessed, and finally, the text is recognized and the result is output. In actual development, detailed parameter settings and function calls need to be made according to the specific library and API used.

1. Methods and Code Examples of Data Preprocessing to Improve Accuracy Data preprocessing is a key step to improve the accuracy of text recognition. The following are some common data preprocessing operations and code examples (continuing with the above assumed library as an example):

// Image grayscale
let grayImage = TextRecognitionLibrary.grayScale(preprocessedImage);

// Noise reduction processing (using simple median filtering as an example here)
let denoisedImage = TextRecognitionLibrary.medianFilter(grayImage);

// Binarization (assuming using an adaptive threshold binarization method)
let binaryImage = TextRecognitionLibrary.adaptiveThreshold(denoisedImage);

// Skew correction (assuming using a correction method based on the Hough transform)
let correctedImage = TextRecognitionLibrary.houghTransform(binaryImage);

Through these preprocessing operations, noise in the image can be effectively removed, the contrast between the text and the background can be enhanced, and the tilted document can be corrected, providing more favorable conditions for text recognition and thus improving the recognition accuracy.

(2) Implementation and Demonstration of the Document Scanning Function

1. Process of Implementing Document Scanning by Calling Interfaces Suppose there is a class named DocumentScanner used to implement the document scanning function (the following is simplified conceptual code):

import { DocumentScanner } from '@ohos.documentscanner';

// Create a document scanning instance
let scanner = new DocumentScanner();

// Start document scanning (assuming the relevant devices and permissions have been initialized)
scanner.startScan().then((result) => {
    let scannedImage = result.image;
    // Display or further process the scanning result (such as saving it as a file, etc.)
    console.log('Scanning completed, image size:', scannedImage.width, scannedImage.height);
});

In actual development, the startScan method may involve more parameter settings, such as scanning resolution, image format, scanning mode (color/grayscale), etc., to meet different needs.

1. Code Snippets of Image Processing and Scanned Copy Generation In the document scanning process, image processing is a key link. The following are some possible image processing code snippets (assuming the relevant functions exist in the DocumentScanner class or the relevant image processing library):

// Edge detection (assuming using the Canny edge detection algorithm)
let edges = scanner.cannyEdgeDetection(scannedImage);

// Crop the document (determine the cropping area according to the edge detection result)
let croppedImage = scanner.cropImage(edges);

// Image enhancement (such as contrast enhancement)
let enhancedImage = scanner.contrastEnhancement(croppedImage);

Through these image processing operations, a high-quality scanned copy is generated, making it more clear and accurate to present the document content.

(3) Processing and Collaboration of Complex Document Structures

1. Methods for Recognizing Complex Document Structures For documents with multi-column layouts, it is necessary to first determine the boundaries of the columns during recognition. The columns can be divided by analyzing the arrangement direction, spacing, and other characteristics of the text. For example, in a multi-column document with a horizontal layout, the spacing of the text in the vertical direction is relatively small, while the spacing between columns is relatively large. By detecting this change in spacing, the positions of the columns can be determined, and then the text in each column can be recognized separately. For table recognition, the border lines of the table can be detected first to determine the number of rows, columns, and cell positions of the table. Then, the text in each cell is recognized. During the recognition process, pay attention to handling complex situations such as merged cells in the table. This can be achieved by analyzing the structural characteristics of the table and the distribution rules of the text. For example, for merged cells, it can be judged and processed according to the positional and content relationships of the surrounding cells.
2. Strategies for Data Interaction and Collaborative Processing In the collaborative processing of text recognition and document scanning, data interaction is crucial. After the document scanning is completed, the image data of the scanned copy is passed to the text recognition module, and at the same time, some information about the document structure, such as whether there is a multi-column layout and whether there is a table, is passed. The text recognition module adopts corresponding recognition strategies according to this information. After the recognition result is returned, it is associated with the scanned copy. For example, when generating an electronic document, the recognized text is formatted according to the original structure of the document and inserted into the corresponding position. For a document containing a table, the recognized table content is filled into the corresponding table structure to maintain the integrity and accuracy of the document. At the same time, a caching mechanism can be established during the processing to avoid repeatedly processing the same data and improve the processing efficiency. For example, for the text area or document structure information that has been recognized, it can be directly obtained from the cache under certain conditions, reducing the consumption of computing resources.

(3) Performance Evaluation and Optimization (Continued)

1. Performance Evaluation Indicators and Methods (Continued) In addition to the recognition accuracy and processing time mentioned above, the stability and resource utilization rate of the system can also be considered as performance evaluation indicators. The stability can be evaluated by running the system for a long time and observing whether there are crashes, freezes, or abnormal errors. For example, when continuously processing a large number of documents, check whether the system can continue to run stably without problems such as quitting halfway or data loss. The resource utilization rate can be measured by monitoring the usage of resources such as CPU, memory, and disk during the system operation. For example, use the performance monitoring tools provided by the system or third-party monitoring software to view the CPU usage rate, memory occupancy, and the frequency of disk I/O operations during the document scanning and text recognition processes. By analyzing this data, the usage efficiency of system resources can be understood, and it can be determined whether there are situations of resource waste or resource bottlenecks.
2. Implementation of Optimization Strategies and Demonstration of Effects (Continued) After implementing the optimization strategies, the optimization effects are demonstrated through actual test data. For example, after optimizing the data transmission method, compare the time for transmitting the scanned document image from the acquisition device to the processing device before and after the optimization. It may be found that the transmission time is shortened by more than 30%, effectively improving the overall efficiency of the system. For optimizing the recognition algorithm, test on the same test dataset and compare the recognition accuracy before and after the optimization. It may be found that the accuracy rate is increased by 5 - 10 percentage points. At the same time, observe the changes in the system resource utilization rate. For example, after optimization, the average CPU usage rate is reduced by about 10%, and the peak memory occupancy is reduced by 20%, indicating that the system uses resources more reasonably after optimization and the performance is significantly improved. The implementation of these optimization measures not only improves the performance of the smart office system but also brings a better user experience and improves office efficiency. It is hoped that through the introduction of this article, it can provide some useful references and lessons for developers in the smart office field and jointly promote the development of smart office technologies. If you encounter other problems in the practice process, you are welcome to communicate and discuss together! Haha!