If Human Can Learn from Few Samples, Why Can’t AI? An Attempt On Similar Object Recognition With Few Training Data Using Meta-Learning

Dutta, Subhayu; Goswami, Saptiva; Debnath, Sonali; Adhikary, Subhrangshu; Majumder, Anandaprova

doi:10.1109/nkcon59507.2023.10396424

Cited by 1 publication

(1 citation statement)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a result, document circulation and information management efficiency may be compromised, leading to delays and errors in workflow operations. The need for a specialized document classification model becomes apparent given the prevalence of complex printed documents in various industries [16].…”

Section: Introductionmentioning

confidence: 99%

VisFormers—Combining Vision and Transformers for Enhanced Complex Document Classification

Dutta,

Adhikary,

Dwivedi

2024

MAKE

Self Cite

View full text Add to dashboard Cite

Complex documents have text, figures, tables, and other elements. The classification of scanned copies of different categories of complex documents like memos, newspapers, letters, and more is essential for rapid digitization. However, this task is very challenging as most scanned complex documents look similar. This is because all documents have similar colors of the page and letters, similar textures for all papers, and very few contrasting features. Several attempts have been made in the state of the art to classify complex documents; however, only a few of these works have addressed the classification of complex documents with similar features, and among these, the performances could be more satisfactory. To overcome this, this paper presents a method to use an optical character reader to extract the texts. It proposes a multi-headed model to combine vision-based transfer learning and natural-language-based Transformers within the same network for simultaneous training for different inputs and optimizers in specific parts of the network. A subset of the Ryers Vision Lab Complex Document Information Processing dataset containing 16 different document classes was used to evaluate the performances. The proposed multi-headed VisFormers network classified the documents with up to 94.2% accuracy, while a regular natural-language-processing-based Transformer network achieved 83%, and vision-based VGG19 transfer learning could achieve only up to 90% accuracy. The model deployment can help sort the scanned copies of various documents into different categories.

show abstract

Section: Introductionmentioning

confidence: 99%