IBM Docling's Upgrade: A Fresh Assessment of Intelligent Document Processing Capabilities

In a previous blog post, A Comprehensive Assessment of Upstage for Intelligent Document Processing, we delved into the capabilities and limitations of Upstage in the realm of intelligent document processing. Today, we shift our focus to the upgraded IBM Docling and present a fresh evaluation within the context of Intelligent Document Processing (IDP).

In the highly competitive and fast-paced business world, the efficiency of handling documents plays a vital role. IBM Docling claims to offer advanced features and capabilities to address the challenges of IDP. This evaluation will comprehensively assess its performance, functionality, and the value it brings to businesses in streamlining workflows and enhancing productivity.

I. Highlights

IBM Docling offers several standout features that make it a valuable tool for document management, especially after the upgrade:

1. Resource Optimization: IBM Docling is designed to operate with relatively low resource consumption. It doesn’t require a large amount of computational power to achieve satisfactory results, which means it can be used by a wide range of users without the need for high-end hardware. This feature remains consistent and crucial for users who want to manage documents efficiently without overburdening their systems.
2. Document Conversion: It serves as a link between commercial and open-source software, providing a practical solution for document conversion. After the upgrade, it can accurately analyze and convert documents, further improving work efficiency and streamlining workflows. It supports a wide variety of document formats, which is beneficial for users dealing with different types of files in their daily work.
3. Comprehensive Format Support: IBM Docling is capable of handling a variety of commonly used document formats such as PDF, DOCX, and PPTX. This allows users to manage and work with different types of documents conveniently and without interruption. The upgraded version may potentially have enhanced compatibility and more accurate handling of these formats.
4. Improved Regular Table Recognition: After the upgrade, Docling has shown improved performance in handling regular tables. It can now parse regular tables more efficiently, enabling users to extract them from documents while preserving the original structure and data integrity. This is a significant improvement for users who frequently work with tabular data in their documents.

II. Limitations

Despite its strengths, the upgraded IBM Docling still faces several challenges common to IDP tools. Users may encounter limitations in specific scenarios, particularly when dealing with highly complex documents.

1. Complex Document Structures: Docling excels in recognizing standard layouts and table structures. However, documents with non-standard or highly intricate designs can pose challenges. For instance, tables with irregular borders or nested elements may not always be accurately interpreted. Even after the upgrade, the tool still struggles with complex document layouts that deviate from the norm.
2. Slow Parsing Speed of PDF to Markdown: A significant shortcoming is the sluggish conversion rate from PDF to Markdown, especially with scanned PDFs. It consumes a substantial amount of time, hampering document processing efficiency and inconveniencing users in need of rapid conversions for work or projects. This problem persists in the upgraded version.
3. Multilingual Processing Limitations: In the context of multilingual documents, Docling has been put to the test with languages such as Spanish, Japanese, and Korean. When dealing with PDF pages that contain images, a notable limitation has been observed. In such cases, the content is entirely recognized as images, and only pages with pure text can have their text accurately identified. This poses a challenge for users who work with multilingual documents that incorporate visual elements, as it restricts the full extraction and processing of text in these scenarios.
4. Complex Table Issues: Although there is an improvement in regular table recognition, when it comes to complex tables, issues still persist. Specifically, the handling of table headers remains problematic, and the tool also struggles with merged cells. These limitations can be a hindrance for users who require comprehensive and precise processing of complex tabular information.
5. Equation Extraction Problems: The equation extraction feature of Docling has proven to be less than satisfactory. Not only does the process of parsing equations take a relatively long time, but the overall extraction effect is also poor. This can be a significant drawback for users working in fields such as mathematics, physics, engineering, or any discipline that heavily relies on precise formula processing and analysis.
6. Image Extraction Challenges: When it comes to image extraction, Docling presents another challenge. Images are unable to be displayed in the parsed Markdown files. This indicates that if users need to access the images from the documents, they may have to perform a separate image extraction process, adding an extra step to their workflow.

III. Comprehensive Evaluation of Docling

1. Performance assessment

a. Processing speed

Docling exhibits a notably slow processing speed, especially evident when dealing with scanned PDFs and documents containing formulas. In testing, a 15 - page editable PDF took 3 minutes and 23 seconds to process, while a 21 - page scanned PDF demanded a staggering 37 minutes and 32 seconds. Even a single - page editable PDF took 39 seconds. Worst of all, an 11 - page PDF with formulas required 14 minutes and 57 seconds. These sluggish times can cause significant delays and productivity losses, especially for tasks that need rapid document handling.

b. Resource usage

Docling is designed to operate on typical hardware configurations with reasonable resource management. However, its slow processing, particularly when handling complex or large documents like those with formulas, can lead to higher - than - expected resource consumption. This can potentially degrade the overall performance of the device, compelling users to either allocate additional resources or endure longer waiting periods during processing.

2. Function assessment

a. Text extraction

Docling continues to offer robust text extraction capabilities, accurately retrieving text from a wide range of document formats. This feature ensures that users can rely on the tool to extract text with precision, maintaining the integrity of the original content. It is an essential aspect for tasks that require in - depth text analysis and manipulation, allowing for seamless processing of textual information within documents.

b. Multilingual assessment

In the context of multilingual documents, Docling has been put to the test with languages such as Spanish, Japanese, and Korean. When dealing with PDF pages that contain images, a notable limitation has been observed. In such cases, the content is entirely recognized as images, and only pages with pure text can have their text accurately identified. This poses a challenge for users who work with multilingual documents that incorporate visual elements, as it restricts the full extraction and processing of text in these scenarios.

Sample PDF - Spanish

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Sample PDF - Spanish

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Sample PDF - Japanese

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Sample PDF - Korean

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c. Table recognition

After the upgrade, Docling has shown improved performance in handling regular tables. It can now parse regular tables more efficiently, enabling users to extract them from documents while preserving the original structure and data integrity. However, when it comes to complex tables, issues still persist. Specifically, the handling of table headers remains problematic, and the tool also struggles with merged cells. These limitations can be a hindrance for users who require comprehensive and precise processing of complex tabular information.

Sample PDF

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Sample PDF

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Sample PDF

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Sample PDF

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d. Equation extraction

The equation extraction feature of Docling has proven to be less than satisfactory. Not only does the process of parsing equations take a relatively long time, but the overall extraction effect is also poor. This can be a significant drawback for users working in fields such as mathematics, physics, engineering, or any discipline that heavily relies on precise formula processing and analysis. As a result, users may need to seek alternative methods or tools to ensure the accurate extraction and utilization of equations in their work.

Sample PDF

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Sample PDF

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e. Image extraction

When it comes to image extraction, Docling presents another challenge. Images are unable to be displayed in the parsed Markdown files. This indicates that if users need to access the images from the documents, they may have to perform a separate image extraction process, adding an extra step to their workflow.

IV. Summary

The upgraded IBM Docling presents a mixed bag of features and capabilities in the domain of Intelligent Document Processing (IDP).

On the positive side, the upgrade brings some notable improvements. The resource optimization feature remains a key advantage, allowing the tool to operate on standard hardware without excessive computational demands. The enhanced performance in regular table recognition is also a significant step forward, making it more convenient for users to handle tabular data. Additionally, its role as a bridge between commercial and open - source software for document conversion, along with comprehensive format support, continues to be valuable for streamlining workflows and boosting work efficiency.

However, the upgraded version still has its share of shortcomings. In terms of performance, the processing speed remains relatively slow, especially when dealing with scanned PDFs and documents containing formulas. This can lead to significant delays and productivity losses, and may even cause increased resource consumption and potential disruptions to the overall system performance. Regarding functionality, while it excels in text extraction, it has limitations in multilingual processing for pages with images, struggles with complex table headers and merged cells, has poor equation extraction in terms of time and effect, and images cannot be displayed in parsed Markdown files. Moreover, integration challenges exist when incorporating it into non - IBM ecosystems, and its dependence on AI models requires regular and potentially resource - intensive updates and maintenance.

Overall, the upgraded IBM Docling has the potential to be a useful tool for basic to moderately complex document management tasks, especially those focused on text extraction and simple table handling. However, for users with more demanding requirements, such as those dealing with complex documents, images, advanced table structures, or precise equation processing, they may need to consider supplementary tools or alternative solutions to fully meet their document management needs.

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