An overhaul work ticket quick recognition system based on ocr technology

Abstract

The application belongs to the technical field of power maintenance information recognition processing, in particular to a kind of fast identification system of maintenance work ticket based on ocr technology, including mobile terminal image acquisition module, local image preprocessing module, offline ocr identification operation module, adaptive layout analysis module, intelligent field classification extraction module, local data cache module and man-machine interaction display module;The system is additionally provided with offline ocr identification operation module, and all local deployment of identification algorithm is carried out through lightweight model deployment unit and offline inference operation unit, and the identification process does not need external network and cloud server, and can be completed independently under outdoor networkless environment;Mobile terminal image acquisition module and local image preprocessing module are matched, and shooting, correction, denoising and identification are all integrated in mobile terminal to complete, simplify shooting identification steps, optimize mobile terminal operation process, get rid of the dependence of traditional equipment on back-end server, and improve the flexibility of field work.

Description

Technical Field

[0001] This invention relates to the field of power maintenance information identification and processing technology, specifically a rapid identification system for maintenance work tickets based on OCR technology. Background Technology

[0002] In industries such as power systems, mining and industrial enterprises, rail transportation, and energy and chemical engineering, equipment maintenance work permits are core paper documents that ensure safe maintenance, standardized operations, and risk management of electrical and mechanical equipment. Maintenance work permits typically contain a large amount of key textual information, including work tasks, work location, equipment number, personnel involved, safety measures, operation time, and approval opinions. They serve as an important basis for ensuring standardized, streamlined, and safe maintenance operations.

[0003] To improve the speed of recognizing maintenance work orders, automatic image-to-text recognition is currently used to replace manual recognition. However, the current automatic image-to-text recognition system still has the following technical problems in use:

[0004] (1) Most systems rely on backend server computing and cannot be recognized offline locally. They cannot be used when there is no network during on-site field inspections and outdoor operations, resulting in poor flexibility. Furthermore, they lack mobile terminal adaptation, and the preprocessing and recognition process after mobile phone shooting is cumbersome.

[0005] (2) There are non-standard work tickets on site with inconsistent font sizes, layout offsets, disordered line spacing, and manual corrections and additions. The existing system template matching is fixed, and once the layout is slightly changed, the recognition fails. It has poor universality and relies on fixed templates.

[0006] To address these issues, a rapid identification system for maintenance work orders based on OCR technology is proposed. Summary of the Invention

[0007] The purpose of this invention is to provide a rapid identification system for maintenance work tickets based on OCR technology, so as to solve the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a rapid recognition system for maintenance work tickets based on OCR technology, comprising a mobile image acquisition module, a local image preprocessing module, an offline OCR recognition and calculation module, an adaptive layout parsing module, an intelligent field classification and extraction module, a local data caching module, and a human-computer interaction display module;

[0009] The mobile image acquisition module is used by field staff to photograph and acquire paper maintenance work orders. It supports multi-angle shooting with mobile phones, automatically completes focus and brightness acquisition, retains the original image information of the work order, and provides an image data source for backend processing.

[0010] The local image preprocessing module has a built-in image correction algorithm, which is used to perform geometric correction, noise reduction, shadow removal, binarization, and cropping enhancement on work ticket images that are skewed, unevenly lit, have wrinkles or shifts, or have large contrast between light and dark. This optimizes the imaging quality of handwritten corrections and blurred fonts and reduces interference features.

[0011] The offline OCR recognition module has a built-in lightweight recognition model. The model is packaged and deployed locally without the need to connect to an external server. It can complete image text detection, character segmentation, and text recognition locally on the mobile device, achieving offline recognition in a network-free environment without relying on cloud computing power.

[0012] The adaptive layout parsing module uses an adaptive layout analysis algorithm to automatically identify table lines, text areas, and blank areas, intelligently divide the text layout position, and dynamically segment non-standard work tickets with different font sizes, layout offsets, line spacing errors, and corrections and additions, without being restricted by fixed layouts.

[0013] The intelligent field classification and extraction module performs semantic classification on the identified disordered text, automatically extracts key fields such as work location, work task, staff, approval time, and safety measures, and completes structured classification output;

[0014] The local data caching module is used to cache preprocessed images, recognized text, and field data. It saves the recognition results locally when there is no network during field operations and automatically uploads them after the network is restored, ensuring that the data is not lost.

[0015] The human-computer interaction display module provides a mobile operation interface for staff, including shooting, previewing, recognition, manual correction, result viewing, and local storage functions, simplifying the shooting and recognition process and adapting to one-handed operation on mobile devices.

[0016] The mobile image acquisition module, serving as the system input, acquires work order images and directly transmits them to the local image preprocessing module for image optimization, eliminating interference from shooting angle, lighting, and dirt. The preprocessed high-definition image is then directly sent to the offline OCR recognition module. All recognition operations are completed locally on the mobile device, eliminating the need for data transmission from a cloud server, thus achieving offline recognition. The offline OCR recognition module transmits the initially recognized text information stream to the adaptive layout parsing module. The adaptive layout parsing module then sends the partitioned text to the intelligent field classification and extraction module to complete the structured extraction of key fields. The recognized structured data is synchronously stored in the local data cache module for offline saving and simultaneously pushed to the human-computer interaction display module for visualization. The local data cache module and the human-computer interaction display module are bidirectionally connected, allowing staff to manually modify incorrect fields on the interface. Modified data is then saved back to the cache module in real time, forming a closed-loop process.

[0017] Preferably, the mobile image acquisition module includes a camera acquisition unit, an autofocus unit, a photosensitivity detection unit, and an original image storage unit;

[0018] The camera acquisition unit is used to capture paper maintenance work tickets; the autofocus unit adjusts the focus in real time to prevent blurry images; the light intensity detection unit collects ambient light intensity to assist in subsequent image optimization; and the original image storage unit temporarily stores the original image to provide a data source for preprocessing.

[0019] Preferably, the local image preprocessing module includes a tilt correction unit, an illumination optimization unit, an image denoising unit, a binarization enhancement unit, and a defect repair unit;

[0020] The tilt correction unit performs geometric correction on offset and skewed tickets; the illumination optimization unit eliminates reflections, shadows, and differences in brightness; the image denoising unit removes stains and noise; the binarization enhancement unit strengthens text edges; and the defect repair unit performs pixel repair on altered, blurred, and missing ink characters to improve the imaging quality of non-standard tickets.

[0021] Preferably, the offline OCR recognition and calculation module includes a lightweight model deployment unit, a local computing power scheduling unit, a text detection unit, a character segmentation and recognition unit, and an offline inference and calculation unit;

[0022] The lightweight model deployment unit embeds the recognition model locally on the mobile device, eliminating the need for cloud loading. The local computing power scheduling unit allocates computing power to the mobile device's hardware. The text detection unit locates all text regions in the image. The character segmentation and recognition unit parses characters line by line and character by character. The offline inference and computation unit completes all recognition and inference in an offline environment, without relying on a backend server.

[0023] Preferably, the adaptive layout parsing module includes a table line detection unit, a blank area determination unit, a text area division unit, a dynamic adaptive segmentation unit, and a layout offset correction unit;

[0024] The table line detection unit identifies the borders of the work order table, the blank area determination unit distinguishes between blank areas and text areas, the text area division unit automatically delineates the range of all text, the dynamic adaptive segmentation unit abandons fixed templates and dynamically partitions non-standard tickets with inconsistent font sizes, disordered line spacing, and layout offsets, and the layout offset correction unit corrects the problem of disordered text layout.

[0025] Preferably, the intelligent field classification and extraction module includes a semantic analysis unit, a key field matching unit, a text classification unit, and a structured integration output unit;

[0026] The semantic analysis unit identifies the semantic logic of the text, the key field matching unit locks in key information such as work location, work task, personnel, time, and safety measures, the text classification unit classifies scattered text, and the structured integration output unit generates well-organized and storable standardized ticket data.

[0027] Preferably, the local data caching module includes a temporary storage unit, an offline storage unit, a data encryption unit, and a breakpoint synchronization upload unit;

[0028] The temporary storage unit caches preprocessed images and recognized text; the offline storage unit permanently retains the recognition results when there is no network; the data encryption unit protects confidential data during maintenance; and the breakpoint synchronous upload unit automatically uploads data in batches after the network is restored to prevent data loss.

[0029] Preferably, the human-computer interaction display module includes a visualization display unit, a touch operation unit, a manual correction and editing unit, a result preview unit, and a local export unit;

[0030] The visualization display unit shows the recognized ticket information, the touch operation unit is adapted for easy one-handed operation on mobile devices, the manual correction and editing unit allows manual modification of recognized erroneous text, the result preview unit allows real-time viewing of the recognition effect, and the local export unit enables quick saving and viewing of ticket data.

[0031] Compared with the prior art, the beneficial effects of the present invention are:

[0032] 1) This system adds an offline OCR recognition and calculation module. Through a lightweight model deployment unit and an offline inference and calculation unit, the recognition algorithm is deployed locally. The recognition process does not require an external network or a cloud server and can be completed independently in outdoor environments without network access. Combined with a mobile image acquisition module and a local image preprocessing module, the entire process of shooting, correction, noise reduction, and recognition is integrated on the mobile terminal, simplifying the shooting and recognition steps, optimizing the mobile terminal operation process, eliminating the dependence of traditional devices on backend servers, and improving the flexibility of field operations.

[0033] 2) This application abandons the traditional fixed template matching method and sets up an adaptive layout parsing module. Relying on the dynamic adaptive segmentation unit and the layout offset correction unit, it automatically identifies tables, text and blank areas. It performs template-free dynamic segmentation of non-standard maintenance work tickets with different font sizes, layout offsets, disordered line spacing, and handwritten corrections and additions. With the defect repair unit of the local image preprocessing module, it performs image quality repair on altered and blurred tickets. It does not restrict fixed layouts and can still be recognized normally even with slight layout changes, which greatly improves the versatility of the system and solves the defect of recognition failure caused by the traditional fixed template. Attached Figure Description

[0034] Figure 1 This is a diagram showing the module composition of this system;

[0035] Figure 2 This is a diagram showing the unit composition of the mobile image acquisition module;

[0036] Figure 3 This is a diagram showing the unit composition of the local image preprocessing module;

[0037] Figure 4 A diagram showing the unit composition of the offline OCR recognition and processing module;

[0038] Figure 5 This is a diagram showing the unit composition of the adaptive layout parsing module.

[0039] Figure 6 A diagram showing the unit composition of the intelligent field classification and extraction module;

[0040] Figure 7 A diagram showing the unit composition of the local data caching module;

[0041] Figure 8 This is a diagram showing the unit composition of the human-computer interaction display module. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] Example:

[0044] Please see Figure 1-8 The present invention provides a technical solution:

[0045] A rapid recognition system for maintenance work orders based on OCR technology includes a mobile image acquisition module, a local image preprocessing module, an offline OCR recognition and calculation module, an adaptive layout parsing module, an intelligent field classification and extraction module, a local data caching module, and a human-computer interaction display module.

[0046] The mobile image acquisition module is used by field staff to photograph and acquire paper maintenance work orders. It supports multi-angle shooting with mobile phones, automatically completes focus and brightness acquisition, retains the original image information of the work order, and provides an image data source for backend processing.

[0047] The local image preprocessing module has a built-in image correction algorithm, which is used to perform geometric correction, noise reduction, shadow removal, binarization, and cropping enhancement on work ticket images that are skewed, unevenly lit, have wrinkles or shifts, or have large contrast between light and dark. This optimizes the imaging quality of handwritten corrections and blurred fonts and reduces interference features.

[0048] The offline OCR recognition module has a built-in lightweight recognition model. The model is packaged and deployed locally without the need to connect to an external server. It can complete image text detection, character segmentation, and text recognition locally on the mobile device, achieving offline recognition in a network-free environment without relying on cloud computing power.

[0049] The adaptive layout parsing module uses an adaptive layout analysis algorithm to automatically identify table lines, text areas, and blank areas, intelligently divide the text layout position, and dynamically segment non-standard work tickets with different font sizes, layout offsets, line spacing errors, and corrections and additions, without being restricted by fixed layouts.

[0050] The intelligent field classification and extraction module performs semantic classification on the identified disordered text, automatically extracts key fields such as work location, work task, staff, approval time, and safety measures, and completes structured classification output;

[0051] The local data caching module is used to cache preprocessed images, recognized text, and field data. It saves the recognition results locally when there is no network during field operations and automatically uploads them after the network is restored, ensuring that the data is not lost.

[0052] The human-computer interaction display module provides a mobile operation interface for staff, including shooting, previewing, recognition, manual correction, result viewing, and local storage functions, simplifying the shooting and recognition process and adapting to one-handed operation on mobile devices.

[0053] The mobile image acquisition module, serving as the system input, acquires work order images and directly transmits them to the local image preprocessing module for image optimization, eliminating interference from shooting angle, lighting, and dirt. The preprocessed high-definition image is then directly sent to the offline OCR recognition module. All recognition operations are completed locally on the mobile device, eliminating the need for data transmission from a cloud server, thus achieving offline recognition. The offline OCR recognition module transmits the initially recognized text information stream to the adaptive layout parsing module. The adaptive layout parsing module then sends the partitioned text to the intelligent field classification and extraction module to complete the structured extraction of key fields. The recognized structured data is synchronously stored in the local data cache module for offline saving and simultaneously pushed to the human-computer interaction display module for visualization. The local data cache module and the human-computer interaction display module are bidirectionally connected, allowing staff to manually modify incorrect fields on the interface. Modified data is then saved back to the cache module in real time, forming a closed-loop process.

[0054] The specific coordination steps for each module's collaborative workflow are as follows:

[0055] Image acquisition and transfer: Field staff use the mobile image acquisition module to photograph paper maintenance work tickets. The module automatically completes focusing, ambient brightness detection and saves the original image, and then transmits the original image to the local image preprocessing module in real time.

[0056] Image optimization processing: The local image preprocessing module sequentially completes tilt correction, lighting optimization, noise reduction, binarization, and blemish repair, eliminating interference from shooting angle, lighting, stains, and blurred text, and then sends the optimized high-definition image to the offline OCR recognition and processing module.

[0057] Local offline text recognition: The offline OCR recognition module calls the local lightweight model, schedules the mobile computing power to complete text region localization, character segmentation and offline inference recognition, outputs the original text information stream, and forwards it to the adaptive layout parsing module;

[0058] Layout partitioning correction: The adaptive layout parsing module detects tables, blank spaces and text areas, dynamically segments and corrects layout offsets for non-standard invoices, sorts out the text layout logic, and sends the partitioned structured text to the intelligent field classification and extraction module.

[0059] Key field extraction and integration: The intelligent field classification and extraction module automatically splits and classifies core fields such as work location, work task, staff, approval time, and safety measures through semantic analysis and keyword matching, generating standardized structured data;

[0060] Data storage and visualization: Structured data is transmitted synchronously via two paths: one path stores the data in a local data cache module, where it is temporarily stored by a temporary storage unit and archived locally by an offline storage unit, while a data encryption unit protects confidential information; the other path pushes the data to a human-computer interaction display module to display the recognition results in real time, supporting preview, viewing, and local export. After the network is restored, the breakpoint synchronization upload unit automatically uploads the cached data in batches.

[0061] Manual correction and closed-loop data backup: If an identification error occurs, the operator can manually edit and correct the content in the human-computer interaction display module; the modified data is then sent back to the local data cache module to update the archive in real time, forming a complete closed loop and ensuring data accuracy.

[0062] The mobile image acquisition module includes a camera acquisition unit, an autofocus unit, a photosensitivity detection unit, and an original image storage unit;

[0063] The camera acquisition unit includes a high-definition CMOS image sensor, an optical lens, a shutter assembly, an image acquisition circuit, and an imaging driver chip; the autofocus unit includes a focus motor, a phase focus sensor, a focus control circuit, and a focus adjustment drive module; the light sensitivity detection unit includes an ambient light sensor, a brightness sampling circuit, and a photoelectric signal conversion chip; and the original image storage unit includes onboard RAM, an image cache chip, and a data read / write circuit.

[0064] The camera acquisition unit is used to capture paper maintenance work tickets; the autofocus unit adjusts the focus in real time to prevent blurry images; the light intensity detection unit collects ambient light intensity to assist in subsequent image optimization; and the original image storage unit temporarily stores the original image to provide a data source for preprocessing.

[0065] The local image preprocessing module includes a tilt correction unit, an illumination optimization unit, an image denoising unit, a binarization enhancement unit, and a defect repair unit;

[0066] The tilt correction unit includes a main control processing chip, a geometric calculation circuit, and a coordinate analysis module; the illumination optimization unit includes an image signal conditioning chip, a brightness / contrast adjustment circuit, and a light and shadow compensation calculation module; the image denoising unit includes a noise reduction processing chip, a filtering circuit, and a pixel calculation unit; the binarization enhancement unit includes a threshold calculation chip, an edge enhancement circuit, and a pixel binarization processing module; and the defect repair unit includes an image repair calculation chip, a pixel filling circuit, and a texture compensation processing module.

[0067] The tilt correction unit performs geometric correction on offset and skewed tickets; the illumination optimization unit eliminates reflections, shadows, and differences in brightness; the image denoising unit removes stains and noise; the binarization enhancement unit strengthens text edges; and the defect repair unit performs pixel repair on altered, blurred, and missing ink characters to improve the imaging quality of non-standard tickets.

[0068] The offline OCR recognition and computing module includes a lightweight model deployment unit, a local computing power scheduling unit, a text detection unit, a character segmentation and recognition unit, and an offline inference and computing unit.

[0069] The lightweight model deployment unit includes an embedded NPU computing chip, model-hardened flash memory, and program loading circuitry; the local computing power scheduling unit includes a multi-core main control CPU, a computing power allocation and management chip, and a hardware resource scheduling circuitry; the text detection unit includes an object detection computing chip, a region positioning circuit, and a coordinate marking module; the character segmentation and recognition unit includes a character parsing chip, a feature comparison circuit, and a character library storage chip; and the offline inference computing unit includes an AI inference acceleration chip, a logic operation circuit, and a local computing bus.

[0070] The lightweight model deployment unit embeds the recognition model locally on the mobile device, eliminating the need for cloud loading. The local computing power scheduling unit allocates computing power to the mobile device's hardware. The text detection unit locates all text regions in the image. The character segmentation and recognition unit parses characters line by line and character by character. The offline inference and computation unit completes all recognition and inference in an offline environment, without relying on a backend server.

[0071] The adaptive layout parsing module includes a table line detection unit, a blank area determination unit, a text area division unit, a dynamic adaptive segmentation unit, and a layout offset correction unit.

[0072] The table line detection unit includes a contour detection chip, a straight line recognition circuit, and an edge extraction module; the blank area determination unit includes a pixel statistics chip, a grayscale value comparison circuit, and a region discrimination module; the text region division unit includes a region segmentation operation chip, a boundary calibration circuit, and a region selection module; the dynamic adaptive segmentation unit includes a dynamic partitioning processing chip, an adaptive algorithm operation circuit, and a region recombination module; and the layout offset correction unit includes a position correction chip, a row and column alignment circuit, and a layout correction operation module.

[0073] The table line detection unit identifies the borders of the work order table, the blank area determination unit distinguishes between blank areas and text areas, the text area division unit automatically delineates the range of all text, the dynamic adaptive segmentation unit abandons fixed templates and dynamically partitions non-standard tickets with inconsistent font sizes, disordered line spacing, and layout offsets, and the layout offset correction unit corrects the problem of disordered text layout.

[0074] The intelligent field classification and extraction module includes a semantic analysis unit, a key field matching unit, a text classification unit, and a structured integration output unit;

[0075] The semantic analysis unit includes a semantic parsing processing chip, a lexicon storage chip, and a semantic logic operation circuit; the key field matching unit includes a keyword retrieval chip, a feature matching circuit, and a rule comparison module; the text classification unit includes a classification operation chip, a data sorting circuit, and a tag binding module; and the structured integration output unit includes a data formatting chip, a data packaging circuit, and a standardized output interface.

[0076] The semantic analysis unit identifies the semantic logic of the text, the key field matching unit locks in key information such as work location, work task, personnel, time, and safety measures, the text classification unit classifies scattered text, and the structured integration output unit generates well-organized and storable standardized ticket data.

[0077] The local data caching module includes a temporary storage unit, an offline storage unit, a data encryption unit, and a breakpoint synchronous upload unit;

[0078] The temporary storage unit includes high-speed RAM memory, a temporary cache chip, and a high-speed data bus; the offline storage unit includes embedded Flash memory, built-in storage chips, and data read / write control circuitry; the data encryption unit includes an encryption algorithm chip, a security key storage module, and a data encryption / decryption circuitry; and the breakpoint synchronous upload unit includes a network communication chip, a data breakpoint marking circuit, a transmission protocol processing module, and a wireless communication antenna.

[0079] The temporary storage unit caches preprocessed images and recognized text; the offline storage unit permanently retains the recognition results when there is no network; the data encryption unit protects confidential data during maintenance; and the breakpoint synchronous upload unit automatically uploads data in batches after the network is restored to prevent data loss.

[0080] The human-computer interaction display module includes a visualization display unit, a touch operation unit, a manual correction and editing unit, a result preview unit, and a local export unit;

[0081] The visualization display unit includes an LCD / AMOLED touch screen, a display driver chip, a screen buffer circuit, and a backlight module; the touch operation unit includes a capacitive touch sensing layer, a touch sampling chip, and a touch signal processing circuit; the manual correction and editing unit includes a touch command parsing chip, a text editing and calculation circuit, and a character input processing module; the result preview unit includes a screen preview buffer chip, a multi-view switching circuit, and a display scaling control module; and the local export unit includes a data export interface, a file packaging chip, and a storage medium read / write circuit.

[0082] The visualization display unit shows the recognized ticket information, the touch operation unit is adapted for easy one-handed operation on mobile devices, the manual correction and editing unit allows manual modification of recognized erroneous text, the result preview unit allows real-time viewing of the recognition effect, and the local export unit enables quick saving and viewing of ticket data.

[0083] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or basic characteristics. Therefore, the embodiments should be considered exemplary and non-limiting in all respects. The scope of the invention is defined by the appended claims rather than the foregoing description. Therefore, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention, and no reference numerals in the claims should be construed as limiting the scope of the claims.

[0084] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A rapid recognition system for maintenance work orders based on OCR technology, characterized in that, It includes a mobile image acquisition module, a local image preprocessing module, an offline OCR recognition and calculation module, an adaptive layout parsing module, an intelligent field classification and extraction module, a local data caching module, and a human-computer interaction display module; The mobile image acquisition module is used by field staff to photograph and acquire paper maintenance work orders. It supports multi-angle shooting with mobile phones, automatically completes focus and brightness acquisition, retains the original image information of the work order, and provides an image data source for backend processing. The local image preprocessing module has a built-in image correction algorithm, which is used to perform geometric correction, noise reduction, shadow removal, binarization, and cropping enhancement on work ticket images that are skewed, unevenly lit, have wrinkles or shifts, or have large contrast between light and dark. This optimizes the imaging quality of handwritten corrections and blurred fonts and reduces interference features. The offline OCR recognition module has a built-in lightweight recognition model. The model is packaged and deployed locally without the need to connect to an external server. It can complete image text detection, character segmentation, and text recognition locally on the mobile device, achieving offline recognition in a network-free environment without relying on cloud computing power. The adaptive layout parsing module uses an adaptive layout analysis algorithm to automatically identify table lines, text areas, and blank areas, intelligently divide the text layout position, and dynamically segment non-standard work tickets with different font sizes, layout offsets, line spacing errors, and corrections and additions, without being restricted by fixed layouts. The intelligent field classification and extraction module performs semantic classification on the identified disordered text, automatically extracts key fields such as work location, work task, staff, approval time, and safety measures, and completes structured classification output; The local data caching module is used to cache preprocessed images, recognized text, and field data. It saves the recognition results locally when there is no network during field operations and automatically uploads them after the network is restored, ensuring that the data is not lost. The human-computer interaction display module provides a mobile operation interface for staff, including shooting, previewing, recognition, manual correction, result viewing, and local storage functions, simplifying the shooting and recognition process and adapting to one-handed operation on mobile devices. The mobile image acquisition module serves as the system input terminal. After acquiring the work ticket image, it directly transmits it to the local image preprocessing module to complete image optimization and eliminate interference from shooting angle, lighting, and dirt. The pre-processed high-definition image is directly sent to the offline OCR recognition and processing module. All recognition operations are completed locally on the mobile device without the need for data transmission from the cloud server, thus achieving offline recognition. The offline OCR recognition and processing module transmits the initially recognized text information stream to the adaptive layout parsing module. The adaptive layout parsing module sends the partitioned text to the intelligent field classification and extraction module to complete the structured extraction of key fields. The identified structured data is synchronously stored in the local data cache module for offline saving, and simultaneously pushed to the human-computer interaction display module for visualization. The local data cache module and the human-computer interaction display module are bidirectionally connected, allowing staff to manually modify incorrect fields on the interface. The modified data is then saved back to the cache module in real time, forming a closed-loop process.

2. The maintenance work order rapid recognition system based on OCR technology according to claim 1, characterized in that: The mobile image acquisition module includes a camera acquisition unit, an autofocus unit, a photosensitivity detection unit, and an original image storage unit; The camera acquisition unit is used to capture paper maintenance work tickets; the autofocus unit adjusts the focus in real time to prevent blurry images; the light intensity detection unit collects ambient light intensity to assist in subsequent image optimization; and the original image storage unit temporarily stores the original image to provide a data source for preprocessing.

3. The maintenance work order rapid recognition system based on OCR technology according to claim 1, characterized in that: The local image preprocessing module includes a tilt correction unit, an illumination optimization unit, an image denoising unit, a binarization enhancement unit, and a defect repair unit; The tilt correction unit performs geometric correction on offset and skewed documents; The illumination optimization unit eliminates reflections, shadows, and differences in brightness; the image denoising unit removes stains and noise; the binarization enhancement unit strengthens text edges; and the defect repair unit performs pixel repair on altered, blurred, and missing ink characters, thereby improving the imaging quality of non-standard invoices.

4. The maintenance work order rapid recognition system based on OCR technology according to claim 1, characterized in that: The offline OCR recognition and computing module includes a lightweight model deployment unit, a local computing power scheduling unit, a text detection unit, a character segmentation and recognition unit, and an offline inference and computing unit. The lightweight model deployment unit embeds the recognition model locally on the mobile device, eliminating the need for cloud loading. The local computing power scheduling unit allocates computing power to the mobile device's hardware. The text detection unit locates all text regions in the image. The character segmentation and recognition unit parses characters line by line and character by character. The offline inference and computation unit completes all recognition and inference in an offline environment, without relying on a backend server.

5. The maintenance work order rapid recognition system based on OCR technology according to claim 1, characterized in that: The adaptive layout parsing module includes a table line detection unit, a blank area determination unit, a text area division unit, a dynamic adaptive segmentation unit, and a layout offset correction unit. The table line detection unit identifies the borders of the work order table, the blank area determination unit distinguishes between blank areas and text areas, the text area division unit automatically delineates the range of all text, the dynamic adaptive segmentation unit abandons fixed templates and dynamically partitions non-standard tickets with inconsistent font sizes, disordered line spacing, and layout offsets, and the layout offset correction unit corrects the problem of disordered text layout.

6. The maintenance work order rapid recognition system based on OCR technology according to claim 1, characterized in that: The intelligent field classification and extraction module includes a semantic analysis unit, a key field matching unit, a text classification unit, and a structured integration output unit; The semantic analysis unit identifies the semantic logic of the text, the key field matching unit locks in key information such as work location, work task, personnel, time, and safety measures, the text classification unit classifies scattered text, and the structured integration output unit generates well-organized and storable standardized ticket data.

7. The maintenance work order rapid recognition system based on OCR technology according to claim 1, characterized in that: The local data caching module includes a temporary storage unit, an offline storage unit, a data encryption unit, and a breakpoint synchronous upload unit; The temporary storage unit caches preprocessed images and recognized text; the offline storage unit permanently retains the recognition results when there is no network connection; the data encryption unit protects confidential data during maintenance; and the breakpoint synchronization upload unit automatically uploads data in batches after the network is restored to prevent data loss.

8. A rapid identification system for maintenance work orders based on OCR technology according to claim 1, characterized in that: The human-computer interaction display module includes a visualization display unit, a touch operation unit, a manual correction and editing unit, a result preview unit, and a local export unit; The visualization display unit shows the recognized ticket information, the touch operation unit is adapted for easy one-handed operation on mobile devices, the manual correction and editing unit allows manual modification of recognized erroneous text, the result preview unit allows real-time viewing of the recognition effect, and the local export unit enables quick saving and viewing of ticket data.

Images