Technical Difficulties and Usage Scenarios of Document Comparison

Document comparison technology is an advanced technique for comparing the differences between two documents. It has great technical difficulties and scene value. The technical difficulties and usage scenarios will be discussed in detail below.

1. Technical difficulties:

The challenges faced by document comparison techniques are not only complex and diverse, but also interrelated. A few of the main technical difficulties are explored in depth below:

• OCR Accuracy : Font and format recognition involves multiple fonts and layout formats that may be used in the document, and OCR requires the ability to flexibly recognize different fonts and layouts; seal and signature recognition often pose challenges because they may contain handwritten content and unique the design of.
• Algorithm complexity : including the optimization of the Diff algorithm, which requires customization to adapt to the specific structure and content of the document text, which requires in-depth optimization and adjustment of the algorithm; complex tree structure matching algorithms need to be used in table comparison, such as Zhang- Shasha algorithm, this process is computationally complex and needs to be implemented efficiently.
• Multi-element collaborative comparison : there may be complex relationships between elements such as text, tables, and images in documents. Identifying and processing these relationships is a major challenge for document comparison; advanced semantic understanding requires the context and business of documents logic analysis, which is still a challenge within the scope of current technology.

 

2. Usage scenarios

The usage scenarios of document comparison technology are extensive and diverse. The following is a detailed analysis of each field:

• Legal document review : Case analysis involves legal teams using document comparison techniques to analyze the role and importance of documents in a case; compliance checks ensure that document content is consistent with applicable laws.
• Enterprise document management : including supply chain management, ensuring consistent documents between suppliers and distributors, which contributes to the smooth operation of the entire supply chain; human resource management can confirm the accuracy and consistency of employee documents.
• Financial Services : Risk management involves financial institutions analyzing documentation risks associated with loans, investments, and other financial products; compliance audits ensure documentation complies with all relevant regulations and standards.
• Healthcare : Document comparison can be used to review documents between healthcare providers and patients, insurance companies, etc.
• Government and public services : For example, public procurement can ensure the transparency and accuracy of documents; regulatory compliance is used to supervise the compliance behavior of enterprises and individuals.

These usage scenarios of document comparison technology demonstrate its broad application potential in different industries and domains. With the further maturity and promotion of the technology, it can be expected that it will play a role in more new scenarios and bring convenience to people's work and life.

3. Preprocessing technology

Preprocessing technology is a key part of document comparison, covering character recognition, layout analysis, text cleaning, table and image recognition, and data standardization in documents.

• OCR optimization and layout analysis: As the first step of preprocessing, character recognition needs to use advanced deep learning and image processing technology to accurately recognize the text and characters in the document. At the same time, layout analysis can identify different areas in the document, such as text, tables, images, etc., for subsequent specific processing.
• Text cleaning: Preprocessing involves accurate comparisons to remove unnecessary punctuation and spaces, reduce noise interference, and also includes specific language preprocessing, such as word stem extraction, word segmentation, etc., to better adapt to the needs of specific text structures.
• Table and image recognition: Tables in documents usually contain important information, and advanced image processing techniques are required to identify and parse the table structure. Stamps and other image elements have special importance in documents, and special image recognition and classification techniques help to detect these elements.
• Data standardization: Data standardization ensures consistency and accuracy throughout the comparison process by converting text and other elements into a standard format.
• The core purpose of preprocessing is to ensure the consistency and accuracy of the data and provide a solid foundation for the subsequent comparison process, which can be achieved through precise text cleaning, character recognition and layout analysis.

4. Text comparison technology

Text comparison is the core step in the document comparison process, which involves a series of advanced algorithms and techniques to analyze and identify textual differences between two documents.

• Word segmentation and tokenization: The document text needs to be word-segmented first to determine the basic units of the text, such as words or characters. Tokenization enables more precise identification and comparison of specific structures and elements of text by labeling each part of the text.
• Difference recognition: Diff algorithm and edit distance are the core of text comparison. Diff algorithm can efficiently identify addition, deletion and modification operations between two text sequences, and edit distance is a measure to quantify the difference between two texts.
• Difference merging and unraveling: Certain equality operations can introduce unnecessary complexity and need to be merged or unraveled to help accurately reflect actual changes between texts.
• Difference post-processing: Post-processing includes type identification and coordinate mapping, which can more clearly identify difference types, such as addition, deletion, or modification, and help to accurately represent the spatial relationship of text.
• Multi-level alignment: Multi-level alignment enables text analysis at different levels, such as character level, vocabulary level, and sentence level, to more precisely capture differences in text structure and content.

5. Form comparison technology

Table comparison plays a key role in document comparison, and tables in documents usually contain important business information and data. The technical details of table comparison are explored in detail below:

• Table structure recognition: table tree generation, a table tree is a data structure that represents the table structure, where nodes represent the rows and cells of the table, and edges represent the relationship between them. OCR result analysis, based on the OCR (Optical Character Recognition) results, analyze and extract the text content in the table and its geometric properties, such as position, size, etc.
• Table comparison algorithm: Zhang-Shasha algorithm, an algorithm used to calculate the edit distance between two trees, can compare the structure and content of two tables. Cell text comparison, through the Diff algorithm to compare the text content of the cell, you can identify the specific differences in the text.
• Post-processing of table comparison: addition and deletion processing, identification and processing of table additions and deletions, including additions and deletions of entire tables, table rows and cells. Position matching, through the matching of adjacent text, find the corresponding position of adding and deleting tables, table rows or cells in another document.
• Multi-level comparison: Multi-level comparison can analyze tables at different levels, such as structure level, row level and cell level, so as to more precisely capture the differences of tables.

6. Seal comparison technology

Seal comparison is a very critical part of the document comparison process, and the seal often represents the legal effect of the document. The following is an advanced technical analysis of seal comparison:

• Seal Extraction: Combining OCR and image recognition, through the combination of OCR technology and image recognition technology, the position, shape and content of the seal can be effectively extracted. Morphological operations, edge detection, extraction and segmentation of stamps can be realized through morphological operations.
• Stamp comparison method: feature extraction, which uses advanced computer vision techniques to extract key features of stamps, such as texture, color, and shape. Machine learning model, using a machine learning model, such as a convolutional neural network (CNN), for feature matching and semantic analysis of seals.
• Post-processing of seal comparison: identification of additions, deletions and modifications, determining the addition, deletion and modification of the seal, and accurately matching the position through the text or other reference points near the seal.
• Seal forgery and tampering detection: deep learning method, the latest seal comparison technology can also learn the complex pattern of the seal through the deep learning method, so as to identify abnormal or suspicious changes.