Extracting text from PDFs is surprisingly complex due to the format's focus on visual representation rather than logical structure. PDFs essentially describe how a page should look, specifying the precise placement of glyphs (often without even identifying them as characters) rather than encoding the underlying text itself. This can lead to difficulties in reconstructing the original text flow, especially with complex layouts involving columns, tables, and figures. Further complications arise from embedded fonts, ligatures, and the potential for text to be represented as paths or images, making accurate and reliable text extraction a significant technical challenge.
The blog post details achieving remarkably fast CSV parsing speeds of 21 GB/s on an AMD Ryzen 9 9950X using SIMD instructions. The author leverages AVX-512, specifically the _mm512_maskz_shuffle_epi8 instruction, to efficiently handle character transpositions needed for parsing, significantly outperforming scalar code and other SIMD approaches. This optimization focuses on efficiently handling quoted fields containing commas and escapes, which typically pose performance bottlenecks for CSV parsers. The post provides benchmark results and code snippets demonstrating the technique.
Hacker News users discussed the impressive speed demonstrated in the article, but also questioned its practicality. Several commenters pointed out that real-world CSV data often includes complexities like quoted fields, escaped characters, and varying data types, which the benchmark seemingly ignores. Some suggested alternative approaches like Apache Arrow or memory-mapped files for better real-world performance. The discussion also touched upon the suitability of using AVX-512 for this task given its power consumption, and the possibility of achieving comparable performance with simpler SIMD instructions. Several users expressed interest in seeing benchmarks with more realistic datasets and comparisons to other CSV parsing libraries. Finally, the highly specialized nature of the code and its reliance on specific hardware were highlighted as potential limitations.
This tutorial demonstrates building a basic text adventure game in C. It starts with a simple framework using printf and scanf for output and input, focusing on creating a game loop that processes player commands. The tutorial introduces core concepts like managing game state with variables, handling different actions (like "look" and "go") with conditional statements, and defining rooms with descriptions. It emphasizes a step-by-step approach, expanding the game's functionality by adding new rooms, objects, and interactions through iterative development. The example uses simple string comparisons to interpret player commands and a rudimentary structure to represent the game world. The tutorial prioritizes clear explanations and aims to be an accessible introduction to game programming in C.
Commenters on Hacker News largely praised the tutorial for its clear, concise, and beginner-friendly approach to C programming and game development. Several appreciated the focus on fundamental concepts and the avoidance of complex libraries, making it accessible even to those with limited C experience. Some suggested improvements like using getline() for safer input handling and adding features like saving/loading game state. The nostalgic aspect of text adventures also resonated with many, sparking discussions about classic games like Zork and the broader history of interactive fiction. A few commenters offered alternative approaches or pointed out minor technical details, but the overall sentiment was positive, viewing the tutorial as a valuable resource for aspiring programmers.
Herb is a new command-line tool and Rust library designed to improve the developer experience of working with ERB (Embedded Ruby) templates. It focuses on accurate and efficient parsing of HTML-aware ERB, addressing issues like incorrect syntax highlighting and code completion in existing tools. Herb offers features such as syntax highlighting, formatting, linting (with custom rules), and symbolic renaming within ERB templates, enabling more productive development and refactoring of complex view logic. By understanding the underlying HTML structure, Herb can provide more contextually relevant results and prevent issues common in tools that treat ERB as plain text or simple HTML. It aims to become an essential tool for Ruby on Rails developers and anyone working extensively with ERB.
Hacker News users generally praised Herb for its innovative approach to templating, particularly its HTML-awareness and the potential for improved refactoring capabilities. Some expressed excitement about its ability to parse and manipulate ERB templates more effectively than existing tools. A few commenters questioned the long-term viability of the project given its reliance on Tree-sitter, citing potential maintenance challenges and parser bugs. Others were curious about specific use cases and integration with existing Ruby tooling. Performance concerns and the overhead introduced by parsing were also mentioned, but overall the reception was positive, with many expressing interest in trying out Herb.
The "Norway problem" in YAML highlights the surprising and often problematic implicit typing system. Specifically, the string "NO" is automatically interpreted as the boolean value false, leading to unexpected behavior when trying to represent the country code for Norway. This illustrates a broader issue with YAML's automatic type coercion, where seemingly innocuous strings can be misinterpreted as booleans, dates, or numbers, causing silent errors and difficult-to-debug issues. The article recommends explicitly quoting strings, particularly country codes, and suggests adopting stricter YAML parsers or linters to catch these potential pitfalls early on. Ultimately, the "Norway problem" serves as a cautionary tale about the dangers of YAML's implicit typing and encourages developers to be more deliberate about their data representation.
HN commenters largely agree with the author's point about YAML's complexity, particularly regarding its surprising behaviors around type coercion and implicit typing. Several users share anecdotes of YAML-induced headaches, highlighting issues with boolean and numeric interpretation. Some suggest alternative data serialization formats like TOML or JSON as simpler and less error-prone options, emphasizing the importance of predictability in configuration files. A few comments delve into the nuances of YAML's specification and its suitability for different use cases, arguing it's powerful but requires careful understanding. Others mention tooling as a potential mitigating factor, suggesting linters and schema validators can help prevent common YAML pitfalls.
Janet's PEG module uses a packrat parsing approach, combining memoization and backtracking to efficiently parse grammars defined in Parsing Expression Grammar (PEG) format. The module translates PEG rules into Janet functions that recursively call each other based on the grammar's structure. Memoization, storing the results of these function calls for specific input positions, prevents redundant computations and significantly speeds up parsing, especially for recursive grammars. When a rule fails to match, backtracking occurs, reverting the input position and trying alternative rules. This process continues until a complete parse is achieved or all possibilities are exhausted. The result is a parse tree representing the matched input according to the provided grammar.
Hacker News users discuss the elegance and efficiency of Janet's PEG implementation, particularly praising its use of packrat parsing for memoization to avoid exponential time complexity. Some compare it favorably to other parsing techniques and libraries like recursive descent parsers and the popular Python library parsimonious, noting Janet's approach offers a good balance of performance and understandability. Several commenters express interest in exploring Janet further, intrigued by its features and the clear explanation provided in the linked article. A brief discussion also touches on error reporting in PEG parsers and the potential for improvements in Janet's implementation.
The post "A love letter to the CSV format" extols the virtues of CSV's simplicity, ubiquity, and resilience. It argues that CSV's plain text nature makes it incredibly portable and accessible across diverse systems and programming languages, fostering interoperability and longevity. While acknowledging limitations like ambiguous data typing and lack of formal standardization, the author emphasizes that these very limitations contribute to its flexibility and adaptability. Ultimately, the post champions CSV as a powerful, enduring, and often underestimated format for data exchange, particularly valuable in contexts prioritizing simplicity and broad compatibility.
Hacker News users generally expressed appreciation for the author's lighthearted yet insightful defense of the CSV format. Several commenters highlighted CSV's simplicity, ubiquity, and ease of use as its core strengths, especially in contrast to more complex formats like XML or JSON. Some pointed out the challenges of handling nuanced data like quoted commas within fields, and the lack of a formal standard, while others offered practical solutions like using a proper CSV parser library. The discussion also touched upon the suitability of CSV for different tasks, with some suggesting alternatives for larger datasets or more complex data structures, but acknowledging CSV's continued relevance for simpler applications. A few users shared their own experiences and frustrations with CSV parsing, reinforcing the need for careful handling and the importance of choosing the right tool for the job.
The Arroyo blog post details a significant performance improvement in decoding columnar JSON data using the Rust-based arrow-rs library. By leveraging lazy decoding and SIMD intrinsics, they achieved a substantial speedup, particularly for nested data and lists, compared to existing methods like serde_json and even Python's pyarrow. This optimization focuses on performance-critical scenarios where large JSON datasets are processed, like data engineering and analytics. The improvement stems from strategically decoding only necessary data elements and employing efficient vectorized operations, minimizing overhead and maximizing CPU utilization. This approach promises faster data loading and processing for applications built on the Apache Arrow ecosystem.
Hacker News users discussed the performance benefits and trade-offs of using Apache Arrow for JSON decoding, as presented in the linked blog post. Several commenters pointed out that the benchmarks lacked real-world complexity and that deserialization often isn't the bottleneck in data processing pipelines. Some questioned the focus on columnar format for single JSON objects, suggesting its advantages are better realized with arrays of objects. Others highlighted the importance of SIMD and memory access patterns in achieving performance gains, while some suggested alternative libraries like simd-json for simpler use cases. A few commenters appreciated the detailed explanation and clear benchmarks provided in the blog post, while acknowledging the specific niche this optimization targets.
argp is a Go library providing a GNU-style command-line argument parser. It supports features like short and long options, flags, subcommands, required arguments, default values, and generating help text automatically. The library aims for flexibility and correctness while striving for good performance and minimal dependencies. It emphasizes handling POSIX-style argument conventions and provides a simple, declarative API for defining command-line interfaces within Go applications.
Hacker News users discussed argp's performance, ease of use, and its similarity to the C library it emulates. Several commenters appreciated the library's speed and small size, finding it a preferable alternative to more complex Go flag parsing libraries like pflag. However, some debated the value of mimicking the GNU style in Go, questioning its ergonomic fit. One user highlighted potential issues with error handling and suggested improvements. Others expressed concerns about compatibility and long-term maintenance. The general sentiment leaned towards cautious optimism, acknowledging argp's strengths while also raising valid concerns.
This post explores a shift in thinking about programming languages from individual entities to sets or families of languages. Instead of focusing on a single language's specific features, the author advocates for considering the shared characteristics and relationships between languages within a broader group. This approach involves recognizing core concepts and abstractions that transcend individual syntax, allowing for easier transfer of knowledge and the development of tools that can operate across multiple languages within a set. The author uses examples like the ML language family and the Lisp dialects to illustrate how shared underlying principles can unify seemingly disparate languages, leading to a more powerful and adaptable approach to programming.
The Hacker News comments discuss the concept of "language sets" introduced in the linked gist. Several commenters express skepticism about the practical value and novelty of the idea, questioning whether it genuinely offers advantages over existing programming paradigms like macros, polymorphism, or code generation. Some find the examples unconvincing and overly complex, suggesting simpler solutions could achieve the same results. Others point out potential performance implications and the added cognitive load of managing language sets. However, a few commenters express interest, seeing potential applications in areas like DSL design and metaprogramming, though they also acknowledge the need for further development and clearer examples to demonstrate its usefulness. Overall, the reception is mixed, with many unconvinced but a few intrigued by the possibilities.
Hillel Wayne presents a seemingly straightforward JavaScript code snippet involving a variable assignment within a conditional statement containing a regular expression match. The unexpected behavior arises from how JavaScript's RegExp object handles global flags. Because the global flag is enabled, subsequent calls to test() within the same regex object continue matching from the previous match's position. This leads to the conditional evaluating differently on subsequent runs, resulting in the variable assignment only happening once even though the conditional appears to be true multiple times. Effectively, the regex remembers its position between calls, causing confusion for those expecting each call to test() to start from the beginning of the string. The post highlights the subtle yet crucial difference between using a regex literal each time versus using a regex object, which retains state.
Hacker News users discuss various aspects of the perplexing JavaScript parsing puzzle. Several commenters analyze the specific grammar rules and automatic semicolon insertion (ASI) behavior that lead to the unexpected result, highlighting the complexities of JavaScript's parsing logic. Some point out that the ++ operator binds more tightly than the optional chaining operator (?.), explaining why the increment applies to the property access result rather than the object itself. Others mention the importance of tools like ESLint and linters for catching such potential issues and suggest that relying on ASI can be problematic. A few users share personal anecdotes of encountering similar unexpected JavaScript behavior, emphasizing the need for careful consideration of these parsing quirks. One commenter suggests the puzzle demonstrates why "simple" languages can be more difficult to master than initially perceived.
The blog post demonstrates how to implement symbolic differentiation using definite clause grammars (DCGs) in Prolog. It leverages the elegant, declarative nature of DCGs to parse mathematical expressions represented as strings and simultaneously construct their derivative. By defining grammar rules for basic arithmetic operations (addition, subtraction, multiplication, division, and exponentiation), including the chain rule and handling constants and variables, the Prolog program can effectively differentiate a wide range of expressions. The post highlights the concise and readable nature of this approach, showcasing the power of DCGs for tackling symbolic computation tasks.
Hacker News users discussed the elegance and power of using definite clause grammars (DCGs) for symbolic differentiation, praising the conciseness and declarative nature of the approach. Some commenters pointed out the historical connection between Prolog and DCGs, highlighting their suitability for symbolic computation. A few users expressed interest in exploring further applications of DCGs beyond differentiation, such as parsing and code generation. The discussion also touched upon the performance implications of using DCGs and compared them to other parsing techniques. Some commenters raised concerns about the readability and maintainability of complex DCG-based systems.
This blog post details how to implement custom syntax highlighting in Emacs using tree-sitter. The author demonstrates creating a minor mode for highlighting TODO items and FIXMEs in comments within C++ code. This involves defining specific queries that target the comment nodes in the tree-sitter parse tree and then associating faces (colors and styles) with the captured nodes. The example provides a practical illustration of leveraging tree-sitter's structured code understanding to achieve more precise and context-aware highlighting than traditional regular expression-based approaches. The post also briefly covers how to incorporate these queries into a theme for broader application and includes a troubleshooting tip for ensuring tree-sitter highlighting is active.
HN commenters largely praised the integration of tree-sitter into Emacs, highlighting the significant improvements in syntax highlighting accuracy and performance. Some expressed excitement over the potential for more advanced features like semantic highlighting and code navigation enabled by tree-sitter's deeper understanding of code structure. A few users shared their personal experiences with setting up and using tree-sitter in Emacs, offering tips and workarounds for common issues. One commenter noted the wider adoption of tree-sitter across various editors and its positive impact on the developer experience. Others discussed the technical details of tree-sitter's implementation, comparing it to traditional regular expression-based highlighting. A couple of comments touched on the potential for future improvements, such as asynchronous parsing and better support for more obscure languages.
This 2015 blog post demonstrates how to leverage Lua's flexible syntax and metamechanisms to create a Domain Specific Language (DSL) for generating HTML. The author uses Lua's tables and functions to create a clean, readable syntax that abstracts away the verbosity of raw HTML. By overloading the concatenation operator and utilizing metatables, the DSL allows users to build HTML elements and structures in a declarative way, mirroring the structure of the output. This approach simplifies HTML generation within Lua, making the code cleaner and more maintainable. The post provides concrete examples showing how to define tags, attributes, and nested elements, offering a practical guide to building similar DSLs for other output formats.
Hacker News users generally praised the article for its clear explanation of building a DSL in Lua, particularly appreciating the focus on leveraging Lua's existing features and metamechanisms. Several commenters shared their own experiences and preferences for using Lua for DSLs, including its use in game development and configuration management. One commenter pointed out potential performance considerations when using this approach, suggesting that precompilation could mitigate some overhead. Others discussed alternative methods for building DSLs, such as using parser generators. The use of Lua's setfenv was highlighted, with some acknowledging its power while others expressing caution due to potential debugging difficulties. A few users also mentioned other languages like Fennel and Janet as interesting alternatives to Lua for similar purposes.
This blog post chronicles the author's weekend project of building a compiler for a simplified C-like language. It walks through the implementation of a lexical analyzer, parser (using recursive descent), and code generator targeting x86-64 assembly. The compiler handles basic arithmetic operations, variable declarations and assignments, if/else statements, and while loops. The post emphasizes simplicity and educational value over performance or completeness, providing a practical example of compiler construction principles in a digestible format. The code is available on GitHub for readers to explore and experiment with.
HN users largely praised the TinyCompiler project for its educational value, highlighting its clear code and approachable structure as beneficial for learning compiler construction. Several commenters discussed extending the compiler's functionality, such as adding support for different architectures or optimizing the generated code. Some pointed out similar projects or resources, like the "Let's Build a Compiler" tutorial and the Crafting Interpreters book. A few users questioned the "weekend" claim in the title, believing the project would take significantly longer for a novice to complete. The post also sparked discussion about the practical applications of such a compiler, with some suggesting its use for educational purposes or embedding in resource-constrained environments. Finally, there was some debate about the complexity of the compiler compared to more sophisticated tools like LLVM.
pdfsyntax is a tool that visually represents the internal structure of a PDF file using HTML. It parses a PDF, extracts its objects and their relationships, and presents them in an interactive HTML tree view. This allows users to explore the document's components, such as fonts, images, and text content, along with the underlying PDF syntax. The tool aims to aid in understanding and debugging PDF files by providing a clear, navigable representation of their often complex internal organization.
Hacker News users generally praised the PDF visualization tool for its clarity and potential usefulness in debugging PDF issues. Several commenters pointed out its helpfulness in understanding PDF internals and suggested potential improvements like adding search functionality, syntax highlighting, and the ability to manipulate the PDF structure directly. Some users discussed the complexities of the PDF format, with one highlighting the challenge of extracting clean text due to the arbitrary ordering of elements. Others shared their own experiences with problematic PDFs and expressed hope that this tool could aid in diagnosing and fixing such files. The discussion also touched upon alternative PDF libraries and tools, further showcasing the community's interest in PDF manipulation and analysis.
Ohm is a parsing toolkit designed for creating parsers in JavaScript and TypeScript that are both powerful and easy to use. It features a grammar definition syntax closely resembling EBNF, enabling developers to express complex syntax rules clearly and concisely. Ohm's built-in support for semantic actions allows users to directly embed JavaScript or TypeScript code within their grammar rules, simplifying the process of building abstract syntax trees (ASTs) and performing other actions during parsing. The toolkit provides excellent error reporting capabilities, helping developers quickly identify and fix syntax errors. Its flexible architecture makes it suitable for various applications, from validating user input to building full-fledged compilers and interpreters.
HN users generally expressed interest in Ohm, praising its user-friendliness, clear documentation, and the power offered by its grammar-based approach to parsing. Several compared it favorably to traditional parser generators like PEG.js and nearley, highlighting Ohm's superior error messages and easier learning curve. Some users discussed potential applications, including building linters, formatters, and domain-specific languages. A few questioned the performance implications of its JavaScript implementation, while others suggested potential improvements like adding support for left-recursive grammars. The overall sentiment leaned positive, with many eager to try Ohm in their own projects.
The blog post details the reverse engineering process of Apple's proprietary Typed Stream format used in various macOS features like Spotlight search indexing and QuickLook previews. The author, motivated by the lack of public documentation, utilizes a combination of tools and techniques including analyzing generated Typed Stream files, using class-dump on relevant system frameworks, and examining open-source components like CoreFoundation, to decipher the format. They ultimately discover that Typed Streams are essentially serialized property lists with a specific header and optional compression, allowing for efficient storage and retrieval of typed data. This reverse engineering effort provides valuable insight into the inner workings of macOS and potentially enables interoperability with other systems.
HN users generally praised the author's reverse-engineering effort, calling it "impressive" and "well-documented." Some discussed the implications of Apple using a custom format, speculating about potential performance benefits or tighter integration with their hardware. One commenter noted the similarity to Google's Protocol Buffers, suggesting Apple might have chosen this route to avoid dependencies. Others pointed out the difficulty in reverse-engineering these formats, highlighting the value of such work for interoperability. A few users discussed potential use cases for the information, including debugging and data recovery. Some also questioned the long-term viability of relying on undocumented formats.
The blog post details methods for eliminating left and mutual recursion in context-free grammars, crucial for parser construction. Left recursion, where a non-terminal derives itself as the leftmost symbol, is problematic for top-down parsers. The post demonstrates how to remove direct left recursion using factorization and substitution. It then explains how to handle indirect left recursion by ordering non-terminals and systematically applying the direct recursion removal technique. Finally, it addresses mutual recursion, where two or more non-terminals derive each other, converting it into direct left recursion, which can then be eliminated using the previously described methods. The post uses concrete examples to illustrate these transformations, making it easier to understand the process of converting a grammar into a parser-friendly form.
Hacker News users discussed the potential inefficiency of the presented left-recursion elimination algorithm, particularly its reliance on repeated string concatenation. They suggested alternative approaches using stacks or accumulating results in a list for better performance. Some commenters questioned the necessity of fully eliminating left recursion in all cases, pointing out that modern parsing techniques, like packrat parsing, can handle left-recursive grammars directly. The lack of formal proofs or performance comparisons with established methods was also noted. A few users discussed the benefits and drawbacks of different parsing libraries and techniques, including ANTLR and various parser combinator libraries.
This blog post explores a simplified variant of Generalized LR (GLR) parsing called "right-nulled" GLR. Instead of maintaining a graph-structured stack during parsing ambiguities, this technique uses a single stack and resolves conflicts by prioritizing reduce actions over shift actions. When a conflict occurs, the parser performs all possible reductions before attempting to shift. This approach sacrifices some of GLR's generality, as it cannot handle all types of grammars, but it significantly reduces the complexity and overhead associated with maintaining the graph-structured stack, leading to a faster and more memory-efficient parser. The post provides a conceptual overview, highlights the limitations compared to full GLR, and demonstrates the algorithm with a simple example.
Hacker News users discuss the practicality and efficiency of GLR parsing, particularly in comparison to other parsing techniques. Some commenters highlight its theoretical power and ability to handle ambiguous grammars, while acknowledging its potential performance overhead. Others question its suitability for real-world applications, suggesting that simpler methods like PEG or recursive descent parsers are often sufficient and more efficient. A few users mention specific use cases where GLR parsing shines, such as language servers and situations requiring robust error recovery. The overall sentiment leans towards appreciating GLR's theoretical elegance but expressing reservations about its widespread adoption due to perceived complexity and performance concerns. A recurring theme is the trade-off between parsing power and practical efficiency.
Keon is a new serialization/deserialization (serde) format designed for human readability and writability, drawing heavy inspiration from Rust's syntax. It aims to be a simple and efficient alternative to formats like JSON and TOML, offering features like strongly typed data structures, enums, and tagged unions. Keon emphasizes being easy to learn and use, particularly for those familiar with Rust, and focuses on providing a compact and clear representation of data. The project is actively being developed and explores potential use cases like configuration files, data exchange, and data persistence.
Hacker News users discuss KEON, a human-readable serialization format resembling Rust. Several commenters express interest, praising its readability and potential as a configuration language. Some compare it favorably to TOML and JSON, highlighting its expressiveness and Rust-like syntax. Concerns arise regarding its verbosity compared to more established formats, particularly for simple data structures, and the potential niche appeal due to the Rust syntax. A few suggest potential improvements, including a more formal specification, tools for generating parsers in other languages, and exploring the benefits over existing formats like Serde. The overall sentiment leans towards cautious optimism, acknowledging the project's potential but questioning its practical advantages and broader adoption prospects.
Summary of Comments ( 20 )
https://news.ycombinator.com/item?id=43973721
HN users discuss the complexities of accurate PDF-to-text conversion, highlighting issues stemming from PDF's original design as a visual format, not a semantic one. Several commenters point out the challenges posed by embedded fonts, tables, and the variety of PDF generation methods. Some suggest OCR as a necessary, albeit imperfect, solution for visually-oriented PDFs, while others mention tools like
pdftotextand Apache PDFBox. The discussion also touches on the limitations of existing libraries and the ongoing need for robust solutions, particularly for complex or poorly generated PDFs. One compelling comment chain dives into the history of PDF and PostScript, explaining how the format's focus on visual fidelity complicates text extraction. Another insightful thread explores the different approaches taken by various PDF-to-text tools, comparing their strengths and weaknesses.The Hacker News post "PDF to Text, a Challenging Problem" linking to an article on the complexities of PDF to text conversion, has generated a significant discussion with a variety of perspectives.
Many commenters agree with the article's premise, highlighting the inherent difficulties in reliably extracting text from PDFs. They point out the wide range of PDF generation methods, from scanned images to programmatically created documents, each presenting unique challenges. Some users share anecdotal experiences of struggling with poor OCR, unexpected formatting changes, and the loss of semantic information during conversion.
One compelling comment thread discusses the difference between "text extraction" and "information retrieval." The argument is that simply pulling out strings of characters isn't enough; true utility comes from understanding the context and meaning within the document. This leads to a discussion of techniques like layout analysis and semantic understanding, which are more complex but offer greater potential for accurate and meaningful text extraction.
Several comments delve into the technical aspects of PDF structure. They mention the challenges posed by embedded fonts, complex layouts, and the lack of a standardized approach to encoding semantic information within PDFs. Some commenters with experience in PDF processing libraries share insights into the limitations and workarounds they've encountered.
A recurring theme is the frustration with the PDF format itself. Some view it as a legacy format ill-suited for modern information retrieval needs. Others acknowledge its continued importance while expressing hope for improved tools and techniques for handling its complexities. There's a brief mention of alternative formats, but the consensus seems to be that PDF remains a dominant force, necessitating ongoing efforts to improve text extraction capabilities.
A few commenters offer practical suggestions, including specific libraries or tools for PDF processing. They also discuss pre-processing techniques like image cleaning and OCR optimization that can improve the accuracy of text extraction.
Finally, some comments offer a more philosophical perspective, reflecting on the trade-offs between a format's visual fidelity and its accessibility for machine processing. The discussion highlights the inherent tension between preserving the visual integrity of a document and enabling efficient information retrieval. Overall, the comments paint a picture of a challenging problem with no easy solutions, but one that continues to motivate developers and researchers to explore new approaches.