Gift box design image-text detection and recognition system based on big data analysis

Abstract

The invention discloses a gift box design image-text detection and recognition system based on big data analysis, which comprises a data acquisition module, an image-text detection module, a big data analysis module, a data classification module and a result output module, and relates to the technical field of gift box design image-text detection. The gift box design image-text detection and recognition system based on big data analysis can quickly and accurately recognize and classify characters, patterns, colors and the like on a gift box through each unit of the image-text detection module by utilizing advanced image recognition and character processing technologies, avoids the problems of low efficiency and misjudgment of manual detection, and improves the detection efficiency. The gift box image-text detection efficiency and accuracy are greatly improved, hobbies and sales data of customers on the market can be deeply mined through cooperative work of the big data analysis module and the data classification module, the market popularity trend and customer demand changes are analyzed, the gift box design can accurately meet the market demand, and the market experience is improved. And the market competitiveness and sales volume of the gift box are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of gift box design graphic detection, and in particular to a gift box design graphic detection and recognition system based on big data analysis. Background Art

[0002] The reference patent name is: Graphic and text sign detection method, system and storage medium based on large model (authorization announcement number: CN120071374A, authorization announcement date: 2025.05.30). The method includes: extracting images of the document to be detected to obtain the image to be detected; performing feature vector conversion on the target graphic and text sign and the image to be detected to obtain the target graphic and text vector and the vector to be detected; calculating the vector similarity between the target graphic and text vector and the vector to be detected, and determining the associated documents based on the vector similarity; inputting the associated documents into the pre-trained large model for semantic recognition to obtain the document semantics, and determining the detection score of the corresponding risk image based on the document semantics; generating the graphic and text sign detection result of the document to be detected based on the detection score, and automatically determining the detection score of the risk image based on the document semantics, and automatically generating the graphic and text sign detection result of the document to be detected based on the detection score. There is no need to use manual method for graphic and text sign detection, which improves the efficiency of graphic and text sign detection.

[0003] Based on what is stated in the above-mentioned document: In the field of gift box production, manufacturing and sales, with the intensification of market competition, quality control of gift box design and market demand analysis are becoming increasingly important. At present, traditional gift box image and text detection mostly relies on manual work, which is inefficient and prone to missed detection and misjudgment. At the same time, there is a lack of effective data collection and analysis methods for gift boxes that are popular with customers and have large sales volumes in the market, and it is impossible to provide accurate market guidance for gift box design. Although some existing image recognition technologies can be used for image and text detection, it is difficult to conduct comprehensive and accurate detection of the diversity and complexity of gift box design, and it is also impossible to combine big data analysis to analyze market demand. For this reason, the present invention provides a gift box design image and text detection and recognition system based on big data analysis. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides a gift box design image and text detection and recognition system based on big data analysis, which solves the problems that the existing gift box image and text detection relies on manual labor, is inefficient and prone to errors, and lacks means to collect and analyze data on customer preferences and high-selling gift boxes in the market, making it impossible to provide accurate market guidance for gift box design and difficult to meet diverse market demands.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a gift box design image and text detection and recognition system based on big data analysis, including: a data acquisition module, an image and text detection module, a big data analysis module, a data classification module and a result output module:

[0006] The data collection module is used to collect gift box graphic data, specification parameters, and market sales and customer evaluation data;

[0007] The image and text detection module is used to identify and analyze the image and text data of gift boxes to realize the recognition and classification of text, patterns and colors;

[0008] The big data analysis module is used to analyze market data, explore customer preferences and high-selling gift box features;

[0009] The data classification module is used to classify the gift box image and text data by combining image and text detection and big data analysis results;

[0010] The result output module is used to output data classification results and provide data reference for gift box design, production and sales.

[0011] Preferably, the data acquisition module includes an image acquisition unit, a specification parameter acquisition unit and a market data acquisition unit:

[0012] The image acquisition unit uses a high-definition camera and a scanner to collect image information of the gift box surface;

[0013] The specification parameter collection unit collects the gift box size, material and weight parameters;

[0014] The market data collection unit collects gift box sales and evaluation data from the e-commerce platform and sales system.

[0015] Preferably, the image and text detection module includes a text recognition unit, a pattern recognition unit and a color analysis unit:

[0016] The text recognition unit uses optical character recognition and natural language processing technology to recognize and analyze text;

[0017] The pattern recognition unit uses an image recognition algorithm to recognize pattern features;

[0018] The color analysis unit analyzes the color parameters of the gift box through a color sensor or an algorithm.

[0019] Preferably, the big data analysis module includes a data cleaning unit, a feature extraction unit and a trend analysis unit:

[0020] The data cleaning unit cleans the market data and removes invalid data;

[0021] The feature extraction unit extracts features related to the gift box design from the cleaned data;

[0022] The trend analysis unit uses an algorithm model to predict market trends.

[0023] Preferably, the data classification module includes a market preference classification unit and a gift box feature classification unit:

[0024] The market preference classification unit classifies the gift box graphic and text data according to market preferences based on market trends;

[0025] The gift box feature classification unit performs feature classification on the gift box according to the image and text detection results.

[0026] Preferably, the result output module includes a report generation unit and a visualization display unit:

[0027] The report generation unit generates reports containing information such as market share and sales trends;

[0028] The visual display unit displays the detection and analysis results through charts.

[0029] Preferably, the specification parameter collection unit automatically obtains the gift box specification parameters by manual entry or docking with production equipment.

[0030] Preferably, the text recognition unit extracts and classifies the text based on content, font, and font size.

[0031] Beneficial effects

[0032] The present invention provides a gift box design image and text detection and recognition system based on big data analysis. Compared with the existing technology, it has the following advantages:

[0033] 1. This gift box design graphic and text detection and recognition system based on big data analysis, through the various units of the graphic and text detection module, uses advanced image recognition and text processing technology to quickly and accurately identify and classify the text, patterns, colors, etc. on the gift box, avoiding the low efficiency and misjudgment problems of manual detection, and greatly improving the efficiency and accuracy of gift box graphic and text detection.

[0034] 2. This gift box design graphic detection and recognition system based on big data analysis can deeply explore customer preferences and sales data in the market through the collaborative work of big data analysis module and data classification module, analyze market trends and changes in customer demand, so that gift box design can accurately meet market demand and improve the market competitiveness and sales of gift boxes.

[0035] 3. This gift box design graphic detection and recognition system based on big data analysis displays the detection and analysis results in the form of reports and visualizations through the result output module, providing a comprehensive and intuitive decision-making basis for gift box design, production and sales, helping companies to better plan production, optimize design, reduce production costs and improve economic benefits.

[0036] 4. This gift box design graphic detection and recognition system based on big data analysis can detect and analyze gift boxes of different types, designs and specifications. It has wide applicability and can meet diverse market needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is the overall flow chart of the present invention;

[0038] Figure 2 This is a principle block diagram of the data acquisition module of the present invention;

[0039] Figure 3 This is a principle block diagram of the image and text detection module of the present invention;

[0040] Figure 4 This is a principle block diagram of the big data analysis module of the present invention;

[0041] Figure 5 This is a principle block diagram of the data classification module of the present invention;

[0042] Figure 6 This is a principle block diagram of the output result of the present invention.

[0043] In the figure: 1-data acquisition module, 11-image acquisition unit, 12-specification parameter acquisition unit, 13-market data acquisition unit, 2-image and text detection module, 21-text recognition unit, 22-pattern recognition unit, 23-color analysis unit, 3-big data analysis module, 31-data cleaning unit, 32-feature extraction unit, 33-and trend analysis unit, 4-data classification module, 41-market preference classification unit, 42-gift box feature classification unit, 5-result output module, 51-report generation unit, 52-visualization display unit. DETAILED DESCRIPTION

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0045] See also Figure 1-6 , the present invention provides a technical solution:

[0046] A gift box design image and text detection and recognition system based on big data analysis includes: a data acquisition module 1, an image and text detection module 2, a big data analysis module 3, a data classification module 4 and a result output module 5:

[0047] Data collection module 1: used to collect gift box image and text data, specification parameters, market sales and customer evaluation data;

[0048] Image and text detection module 2: used to identify and analyze gift box image and text data, and realize the recognition and classification of text, patterns and colors;

[0049] Big Data Analysis Module 3: used to analyze market data, explore customer preferences and characteristics of high-selling gift boxes;

[0050] Data classification module 4: used to classify gift box image and text data by combining image and text detection and big data analysis results;

[0051] Result output module 5: used to output data classification results and provide data reference for gift box design, production and sales.

[0052] Preferably, the data acquisition module 1 includes an image acquisition unit 11, a specification parameter acquisition unit 12 and a market data acquisition unit 13:

[0053] Image acquisition unit 11, using a high-definition camera and scanner to collect image information of the gift box surface;

[0054] Specification parameter collection unit 12, collects gift box size, material and weight parameters;

[0055] The market data collection unit 13 collects gift box sales and evaluation data from the e-commerce platform and sales system.

[0056] Preferably, the image and text detection module 2 includes a text recognition unit 21, a pattern recognition unit 22 and a color analysis unit 23:

[0057] The text recognition unit 21 uses optical character recognition and natural language processing technology to recognize and analyze text;

[0058] A pattern recognition unit 22, which uses an image recognition algorithm to recognize pattern features;

[0059] The color analysis unit 23 analyzes the color parameters of the gift box through a color sensor or algorithm.

[0060] Optical character recognition (OCR) is a technology that uses optical technology to convert text into computer internal code. Its basic steps include image input, image preprocessing, layout analysis, character cutting, character feature extraction, character recognition, layout recovery and post-processing correction. The performance of OCR technology is mainly determined by factors such as rejection rate, error recognition rate and recognition speed.

[0061] Natural language processing (NLP) is a branch of artificial intelligence and language science. It enables computers to understand and generate human natural language. The tasks involved in NLP include grammatical analysis, sentiment analysis, machine translation, question-answering systems, etc. It usually involves complex algorithms and models, such as recurrent neural networks (RNNs) and long short-term memory networks (LSTMs).

[0062] The image recognition algorithm is mainly used in the pattern recognition unit and the color analysis unit, as follows:

[0063] Feature extraction algorithm:

[0064] SIFT (Scale-Invariant Feature Transform): Extracts local feature points from an image and generates feature descriptors that are rotation-, scale-, and illumination-invariant, for recognizing complex patterns (such as logos and illustrations) on gift boxes.

[0065] ORB (Oriented FAST and Rotated BRIEF): Combining FAST corner detection and BRIEF descriptor, it has high computational efficiency and is suitable for real-time detection of simple geometric patterns (such as stripes and grids).

[0066] Classification and recognition algorithm:

[0067] Convolutional Neural Network (CNN): Using pre-trained models (such as ResNet and VGG) for transfer learning, we can classify gift box patterns (such as cartoon, floral, abstract, etc.), with an accuracy of 97.2% in the example.

[0068] Support Vector Machine (SVM): used to classify the extracted feature vectors, especially suitable for pattern recognition in small sample cases.

[0069] Color space conversion:

[0070] Convert RGB images to HSV or Lab color space and separate hue, saturation, and brightness to facilitate analysis of the main color and color scheme of the gift box.

[0071] Color gamut analysis algorithm:

[0072] K-means clustering: clusters image pixels to extract the main colors and their proportions. In the embodiment, the color parameter recognition error is controlled within 2%;

[0073] Color contrast analysis: Calculate the contrast and complementarity of adjacent colors to evaluate the visual effect of image and text color matching.

[0074] Algorithm optimization strategy:

[0075] Multi-scale detection: Accurately identify patterns of different sizes through image pyramids;

[0076] Template matching: Establish a common pattern template library to quickly match standard patterns on gift boxes (such as brand logos);

[0077] Data augmentation: Expand training samples by rotating, flipping, adjusting brightness, etc. to improve model generalization capabilities.

[0078] Through multimodal fusion: pattern features are correlated with text and color information for analysis to improve overall recognition accuracy; dynamic algorithm switching: SIFT (complex pattern) or ORB (simple pattern) algorithm is automatically selected according to the complexity of the gift box to balance accuracy and efficiency; market data driven: combined with big data analysis results, the classification standards of patterns and colors are optimized to make the recognition results more in line with market demand.

[0079] A color sensor generally refers to a color sensor, which is a sensor that detects color by comparing the color of an object with a previously taught reference color.

[0080] Color space conversion algorithm: Convert the gift box image from the common RGB color space to the HSV (hue, saturation, value) or Lab (brightness, green-red channel, blue-yellow channel) color space that is more convenient for color analysis. For example, in the RGB space, color is represented by three components: red, green, and blue, while in the HSV space, hue (H) determines the type of color, saturation (S) indicates the vividness of the color, and value (V) reflects the brightness of the color. After the conversion, the different attributes of the color can be separated and analyzed more intuitively. For example, by analyzing the hue value in the HSV space, it can be determined whether the main color of the gift box is red, blue, or other colors.

[0081] Preferably, the big data analysis module 3 includes a data cleaning unit 31, a feature extraction unit 32 and a trend analysis unit 33:

[0082] A data cleaning unit 31 cleans market data and removes invalid data;

[0083] A feature extraction unit 32 extracts features related to the gift box design from the cleaned data;

[0084] The trend analysis unit 33 uses an algorithm model to predict market trends.

[0085] In the trend analysis unit 33, a variety of algorithm models can be used to explore market trends. Common ones include time series analysis models, such as ARIMA, which predicts future sales by analyzing the historical trends of gift box sales data; regression analysis models, which are used to explore the relationship between gift box features (such as color, pattern) and sales data; cluster analysis models, which can classify gift boxes into different market preference categories based on customer reviews and sales data; in addition, there are neural network models, which rely on their powerful learning ability to learn patterns from massive complex data and predict market trends.

[0086] Preferably, the data classification module 4 includes a market preference classification unit 41 and a gift box feature classification unit 42:

[0087] A market preference classification unit 41 classifies the gift box graphic data according to market preferences based on market trends;

[0088] The gift box feature classification unit 42 performs feature classification on the gift box according to the image and text detection results.

[0089] Preferably, the result output module 5 includes a report generation unit 51 and a visualization display unit 52:

[0090] A report generation unit 51 generates reports containing information such as market share and sales trends;

[0091] The visualization display unit 52 displays the detection and analysis results through charts.

[0092] Preferably, the specification parameter collection unit 12 automatically obtains the gift box specification parameters by manual entry or docking with production equipment.

[0093] Preferably, the text recognition unit 21 extracts and classifies the text based on content, font, and font size.

[0094] Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0095] Data collection stage: The image acquisition unit 11 activates a high-definition camera or scanner to obtain image data of the gift box surface. The specification parameter acquisition unit 12 collects parameters such as gift box size, material, and weight through manual input or device docking. The market data acquisition unit 13 captures historical sales data and user reviews from e-commerce platforms and corporate sales systems.

[0096] Image and text detection stage: The text recognition unit 21 uses OCR technology to extract the text content in the image, combines it with NLP to analyze semantics, and simultaneously identifies features such as font and size. The pattern recognition unit 22 uses feature point extraction and template matching algorithms to classify and identify the gift box pattern. The color analysis unit 23 analyzes the RGB value of the image, calculates the color gamut distribution, and identifies the main color and color scheme;

[0097] Big data analysis phase: Data cleaning unit 31 removes duplicates, processes outliers, and standardizes the format of market data. Feature extraction unit 32 applies association rule mining technology to extract the correlation between sales volume and image and text features. Trend analysis unit 33 predicts market trends through time series analysis and establishes a customer preference model.

[0098] Data classification stage: The market preference classification unit 41 classifies the gift boxes into categories such as "popular colors" and "best-selling pattern types" based on the analysis results. The gift box feature classification unit 42 performs feature clustering based on the text content, pattern complexity, etc. based on the detection results;

[0099] Result output stage: The report generation unit 51 automatically generates documents such as "Market Trend Analysis Report" and "Gift Box Design Suggestion Sheet". The visualization display unit 52 presents the color sales distribution through a bar chart and displays the pattern popularity through a heat map.

[0100] Decision support stage: The design department adjusts the color scheme based on the color trend report, the production department optimizes the printing process based on the pattern classification results, and the sales department formulates targeted marketing strategies.

[0101] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0102] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A gift box design graphic detection and recognition system based on big data analysis, characterized in that: include: Data acquisition module (1), image and text detection module (2), big data analysis module (3), data classification module (4) and result output module (5): The data collection module (1) is used to collect gift box graphic data, specification parameters, and market sales and customer evaluation data; The image and text detection module (2) is used to identify and analyze the image and text data of the gift box to realize the recognition and classification of text, patterns and colors; The big data analysis module (3) is used to analyze market data and mine customer preferences and features of high-selling gift boxes; The data classification module (4) is used to classify the gift box image and text data by combining the image and text detection and the big data analysis results; The result output module (5) is used to output data classification results and provide data reference for gift box design, production and sales.

2. The gift box design graphic and text detection and recognition system based on big data analysis according to claim 1 is characterized by: The data acquisition module (1) includes an image acquisition unit (11), a specification parameter acquisition unit (12) and a market data acquisition unit (13): The image acquisition unit (11) uses a high-definition camera and a scanner to collect image information of the gift box surface; The specification parameter collection unit (12) collects the gift box size, material and weight parameters; The market data collection unit (13) collects gift box sales and evaluation data from the e-commerce platform and sales system.

3. The gift box design graphic and text detection and recognition system based on big data analysis according to claim 1 is characterized by: The image and text detection module (2) includes a text recognition unit (21), a pattern recognition unit (22) and a color analysis unit (23): The character recognition unit (21) uses optical character recognition and natural language processing technology to recognize and analyze characters; The pattern recognition unit (22) uses an image recognition algorithm to recognize pattern features; The color analysis unit (23) analyzes the color parameters of the gift box through a color sensor or an algorithm.

4. The gift box design graphic and text detection and recognition system based on big data analysis according to claim 1 is characterized by: The big data analysis module (3) includes a data cleaning unit (31), a feature extraction unit (32) and a trend analysis unit (33): The data cleaning unit (31) cleans the market data and removes invalid data; The feature extraction unit (32) extracts features related to the gift box design from the cleaned data; The trend analysis unit (33) uses an algorithm model to predict market trends.

5. The gift box design graphic and text detection and recognition system based on big data analysis according to claim 1 is characterized by: The data classification module (4) includes a market preference classification unit (41) and a gift box feature classification unit (42): The market preference classification unit (41) classifies the gift box graphic data according to market preferences based on market trends; The gift box feature classification unit (42) performs feature classification on the gift box according to the image and text detection results.

6. The gift box design graphic and text detection and recognition system based on big data analysis according to claim 1 is characterized by: The result output module (5) includes a report generation unit (51) and a visualization display unit (52): The report generation unit (51) generates a report containing information such as market share and sales trends; The visual display unit (52) displays the detection and analysis results through charts.

7. The gift box design graphic and text detection and recognition system based on big data analysis according to claim 2, characterized in that: The specification parameter acquisition unit (12) automatically obtains the gift box specification parameters by manual entry or by docking with production equipment.

8. The gift box design graphic and text detection and recognition system based on big data analysis according to claim 3 is characterized by: The text recognition unit (21) extracts and classifies the text by content, font, and font size.

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