Extracting OCR Indexes Using AI Transformer for Optimal Efficiency (Though I have rewritten the title to be more SEO friendly and attractive to humans, note that the new title may still require further optimization for specific SEO requirements or contexts.)

Extracting OCR Indexes Using AI Transformer for Optimal Efficiency

(Though I have rewritten the title to be more SEO friendly and attractive to humans, note that the new title may still require further optimization for specific SEO requirements or contexts.)

Introduction:

Donut versus Pix2Struct: Comparing Transformer Models for Document Understanding

In this second part, we delve into training and evaluating the Donut and Pix2Struct transformer models on a custom dataset for key index extraction. The process begins by preparing the custom data, which is then uploaded to a new huggingface dataset. We provide a link to the colab notebook used for this purpose.

After training the Donut model for 75 minutes, we achieved a validation metric (edit distance) of 0.116. The results for the validation set show that Donut performs exceptionally well in correctly identifying documents as either a patent or a datasheet, achieving a 100% accuracy in classification. Notably, Donut can classify a document as a datasheet even without the exact word being present.

To gain more insights into the model’s performance, we created a routine to generate an HTML-formatted report table. This table provides a detailed analysis of how the model succeeds or fails in specific cases. Additionally, color codes are used to facilitate quick interpretation of the results.

Stay tuned for the next part, where we will delve deeper into analyzing the model’s performance and comparing it with Pix2Struct.

Full Article: Extracting OCR Indexes Using AI Transformer for Optimal Efficiency

(Though I have rewritten the title to be more SEO friendly and attractive to humans, note that the new title may still require further optimization for specific SEO requirements or contexts.)

Donut versus Pix2Struct: Training and Comparing Transformer Models for Key Index Extraction

Introduction

In the previous article, we discussed the performance of two transformer models, Donut and Pix2Struct, in understanding documents. In this second part, we will delve into training these models and compare their results for the task of key index extraction.

Preparing the Custom Data

To train the transformer models, we need to prepare the custom data first. This involves creating two folders of the dataset and zipping them. I have uploaded the zipped dataset to a new huggingface dataset, which can be accessed here. I have also provided the colab notebook used in the process, which can be found here. This notebook not only downloads the dataset but also sets up the environment, loads the Donut model, and trains it.

Training and Evaluation

After finetuning the Donut model for 75 minutes, I stopped it when the edit distance validation metric reached 0.116. This metric indicates how closely the predicted output matches the ground truth.

Results and Analysis

On a field level, the Donut model demonstrates impressive accuracy. For the validation set, it correctly identifies documents as either a patent or a datasheet, achieving 100% accuracy in classification. It is worth noting that the model does not rely on the presence of specific words, such as “datasheet,” to classify a document correctly. This flexibility is due to the model’s finetuning process, which enables it to recognize various patterns associated with different document types.

While other fields also show satisfactory results, analyzing them solely based on a graph can be challenging. To gain a deeper understanding of the model’s performance, I created a routine in the notebook to generate an HTML-formatted report table. This table provides comprehensive information for each document in the validation set.

The report table includes the recognized (inferred) data on the left along with the corresponding ground truth. On the right side, an image is displayed to provide additional context. To easily grasp the performance, I implemented color codes to highlight correct and incorrect predictions.

Conclusion

In this article, we explored the training process of Donut, a transformer model, for the task of key index extraction. The model showcased remarkable accuracy in classifying documents, achieving a perfect score in identifying patents and datasheets. The detailed report table further facilitated the analysis of the model’s performance on a case-by-case basis. By combining powerful transformer models like Donut with thorough evaluation techniques, we can improve document understanding and streamline information extraction processes.

Summary: Extracting OCR Indexes Using AI Transformer for Optimal Efficiency

(Though I have rewritten the title to be more SEO friendly and attractive to humans, note that the new title may still require further optimization for specific SEO requirements or contexts.)

In this article, the author compares two transformer models, Donut and Pix2Struct, on the task of key index extraction from custom data. The author provides a step-by-step guide on how to train these models and evaluates their results. After finetuning the Donut model for 75 minutes, the author observes a validation metric of 0.116 for edit distance. The classification accuracy for identifying document types (patent or datasheet) is 100% with Donut. The author further analyzes the results by generating an HTML-formatted report table that showcases the model’s performance on specific cases. The article provides valuable insights into the understanding of documents by these transformer models.

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