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DocJev is a free, open-source Python tool for classifying and splitting complex document packets, supporting PDF, DOCX, and PPTX with optional OCR.
This paper introduces a multi-agent system called CB-MAS for document sensitivity classification, which addresses the limitation of fixed input length in transformer models like BERT by using iterative consultation and channel boosting.
DocJev is a lightning-fast open-source library for document classification and splitting using Jev, with OCR backends like liteparse and LlamaParse, claiming 6x speed improvements over GPT-5.6-luna with equivalent accuracy.
Using frontier agents to build small classifiers for data curation can reduce costs significantly compared to LLM labeling, as demonstrated in a case study on the FinePDFs-Edu dataset.
HIRA is a training-free, on-premises retrieval-augmented cascade system for document classification in regulated industries that uses human feedback and on-premises LLMs to improve accuracy while reducing model retraining and LLM calls.
SIFT introduces a self-improving document classifier that uses a cheap SPLADE-LightGBM pipeline and an LLM judge to continuously teach itself, while a frozen-gate safety mechanism prevents silent regression, enabling autonomous retraining without human labeling overhead.
This paper identifies and corrects label errors and test-train overlap in the RVL-CDIP document classification dataset, finding 12% label errors and 35% duplication. Correction improves classification accuracy and out-of-distribution generalization.
This paper introduces a KAN-enhanced BiGRU architecture for classifying and summarizing multilingual legal documents from Bangladesh, achieving modest accuracy and ROUGE scores and demonstrating that the KAN block improves classification accuracy over the baseline BiGRU.
This systematic review of 139 studies proposes a unified framework and meta-analysis for document classification via multimodal and multiview information fusion, finding that fusion improves accuracy (mean gain of +5.28 percentage points) but highlights reproducibility challenges.