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Reading Order Independent Metrics for Evaluating Information Extraction in Handwritten Documents


Core Concepts
This paper proposes a set of reading order independent metrics tailored to evaluating information extraction performance in handwritten documents, addressing the limitations of existing metrics that are sensitive to reading order errors.
Abstract
The paper introduces a set of reading order independent metrics for evaluating information extraction (IE) in handwritten documents. Existing metrics used for this task often rely on word positions or text alignment, which can introduce biases and do not reflect the expected final application of IE systems. The proposed metrics include: OIECER and OIEWER: Adaptations of the ECER and EWER metrics that use a non-sequential alignment between ground truth and predicted named entities. OINerval: An adaptation of the Nerval metric that computes precision, recall, and F1-score using a non-sequential alignment. Bag-of-tagged-words and bag-of-entities metrics: Alternative representations that compute error rates and precision/recall/F1 without relying on alignment. The authors evaluate these metrics on four public datasets and a real-world use case, and provide an in-depth analysis of their behavior. They recommend the use of OIECER, OIEWER, and OINerval as the most effective reading order independent metrics for IE evaluation. The key insights are: Existing metrics that rely on word positions or reading order are sensitive to segmentation errors and do not reflect the expected application of IE systems. The proposed reading order independent metrics provide a more reliable and unbiased evaluation of IE performance. The recommended OIECER, OIEWER, and OINerval metrics offer a comprehensive assessment of both text recognition and semantic labeling.
Stats
The IAM dataset contains 747 training, 116 validation, and 336 test pages with 18 named entity types. The Simara dataset contains 3778 training, 811 validation, and 804 test pages with 7 fields. The Esposalles dataset contains 75 training, 25 validation, and 25 test pages with 35 combined named entity types. The POPP dataset contains 128 training, 16 validation, and 16 test pages with 10 column names as named entities. The real-world French Military Records dataset contains 118 training and 29 validation full-page images with 5 named entity types.
Quotes
"Information Extraction (IE) refers to the identification of parts of digital text that contain specific knowledge." "Coupled approaches that solve the task in one step have also seen successful application in recent years at solving the task, mainly when applied to historical documents." "When performing IE with the intent of building a knowledge database, the goal is to identify which Named Entities appear in each document. Therefore, as long as the segmentation errors do not lead to splitting Named Entities, the order in which they are recognized should not matter."

Deeper Inquiries

How can the proposed reading order independent metrics be extended to handle nested named entities?

The proposed reading order independent metrics can be extended to handle nested named entities by modifying the assignment cost computation to account for the hierarchical structure of nested entities. Currently, the metrics focus on aligning Named Entities in a non-sequential manner, allowing for one-to-one relations between entities in any order. To handle nested entities, the cost computation for matching entities would need to consider not only the Named Entity itself but also its nested entities. One approach to extend the metrics for nested entities could involve assigning different costs based on the level of nesting. For example, a higher cost could be assigned for matching a parent entity with a nested entity compared to matching two entities at the same level. By incorporating the hierarchical relationships between entities into the cost computation, the metrics can effectively evaluate the extraction of nested named entities in documents.

How do the performance characteristics of the recommended metrics (OIECER, OIEWER, OINerval) compare when evaluating models that operate at different segmentation levels (page, line, word)?

The performance characteristics of the recommended metrics (OIECER, OIEWER, OINerval) may vary when evaluating models that operate at different segmentation levels such as page, line, and word. Page Level: When evaluating models at the page level, the recommended metrics can provide a comprehensive assessment of the model's performance in extracting Named Entities from entire pages. OIECER and OIEWER can capture errors in entity recognition and transcription quality at a macro level, while OINerval can evaluate the precision, recall, and F1 scores of the extracted entities. Line Level: At the line level, the metrics may be more sensitive to errors in entity recognition and transcription due to the finer segmentation. OIECER and OIEWER can still effectively evaluate the model's performance in capturing Named Entities within lines of text, while OINerval can provide insights into the precision and recall of entity extraction at this level. Word Level: When operating at the word level, the metrics may focus more on the accuracy of individual Named Entities and their transcription. OIECER and OIEWER can highlight errors in entity recognition and transcription at a granular level, while OINerval can assess the precision, recall, and F1 scores of individual named entities extracted at the word level. Overall, the recommended metrics can adapt to different segmentation levels and provide valuable insights into the model's performance in extracting Named Entities at varying levels of granularity.

What are the potential applications of these reading order independent metrics beyond information extraction, such as in other document understanding tasks?

The reading order independent metrics proposed in the context of information extraction in handwritten documents have broader applications beyond this specific task. Some potential applications of these metrics in other document understanding tasks include: Document Classification: The metrics can be used to evaluate the performance of document classification models by assessing the accuracy of classifying documents into different categories independent of the reading order errors. Entity Linking: In tasks involving entity linking, where entities in text need to be linked to knowledge bases, the metrics can help evaluate the accuracy of linking entities regardless of their order in the text. Relation Extraction: For tasks involving relation extraction between entities, the metrics can be adapted to evaluate the precision and recall of extracting relationships between entities without being sensitive to the order in which they appear. Document Summarization: In document summarization tasks, the metrics can assess the effectiveness of summarizing key information from documents, ensuring that the extracted information is accurate and independent of the reading order. By applying these reading order independent metrics to a variety of document understanding tasks, researchers and practitioners can gain insights into the performance of their models in a more robust and unbiased manner.
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