Exploring Light-Weight Object Recognition for Real-Time Document Detection
- URL: http://arxiv.org/abs/2509.06246v1
- Date: Sun, 07 Sep 2025 23:58:28 GMT
- Title: Exploring Light-Weight Object Recognition for Real-Time Document Detection
- Authors: Lucas Wojcik, Luiz Coelho, Roger Granada, David Menotti,
- Abstract summary: Real-time document detection and rectification is a niche that is largely unexplored by the literature.<n>We adapt IWPOD-Net, a license plate detection network, and train it for detection on NBID, a synthetic ID card dataset.<n>We show that our model is smaller and more efficient than current state-of-the-art solutions while retaining a competitive OCR quality metric.
- Score: 1.623310884498926
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Object Recognition and Document Skew Estimation have come a long way in terms of performance and efficiency. New models follow one of two directions: improving performance using larger models, and improving efficiency using smaller models. However, real-time document detection and rectification is a niche that is largely unexplored by the literature, yet it remains a vital step for automatic information retrieval from visual documents. In this work, we strive towards an efficient document detection pipeline that is satisfactory in terms of Optical Character Recognition (OCR) retrieval and faster than other available solutions. We adapt IWPOD-Net, a license plate detection network, and train it for detection on NBID, a synthetic ID card dataset. We experiment with data augmentation and cross-dataset validation with MIDV (another synthetic ID and passport document dataset) to find the optimal scenario for the model. Other methods from both the Object Recognition and Skew Estimation state-of-the-art are evaluated for comparison with our approach. We use each method to detect and rectify the document, which is then read by an OCR system. The OCR output is then evaluated using a novel OCR quality metric based on the Levenshtein distance. Since the end goal is to improve automatic information retrieval, we use the overall OCR quality as a performance metric. We observe that with a promising model, document rectification does not have to be perfect to attain state-of-the-art performance scores. We show that our model is smaller and more efficient than current state-of-the-art solutions while retaining a competitive OCR quality metric. All code is available at https://github.com/BOVIFOCR/iwpod-doc-corners.git
Related papers
- ABCD-LINK: Annotation Bootstrapping for Cross-Document Fine-Grained Links [57.514511353084565]
We introduce a new domain-agnostic framework for selecting a best-performing approach and annotating cross-document links.<n>We apply our framework in two distinct domains -- peer review and news.<n>The resulting novel datasets lay foundation for numerous cross-document tasks like media framing and peer review.
arXiv Detail & Related papers (2025-09-01T11:32:24Z) - Improving Document Retrieval Coherence for Semantically Equivalent Queries [63.97649988164166]
We propose a variation of the Multi-Negative Ranking loss for training DR that improves the coherence of models in retrieving the same documents.<n>The loss penalizes discrepancies between the top-k ranked documents retrieved for diverse but semantic equivalent queries.
arXiv Detail & Related papers (2025-08-11T13:34:59Z) - Learning Refined Document Representations for Dense Retrieval via Deliberate Thinking [58.69615583599489]
Deliberate Thinking based Retriever (Debater) is a novel approach that enhances document representations by incorporating a step-by-step thinking process.<n>Debater significantly outperforms existing methods across several retrieval benchmarks.
arXiv Detail & Related papers (2025-02-18T15:56:34Z) - Geometry Restoration and Dewarping of Camera-Captured Document Images [0.0]
This research focuses on developing a method for restoring the topology of digital images of paper documents captured by a camera.<n>Our methodology employs deep learning (DL) for document outline detection, followed by computer vision (CV) to create a topological 2D grid.
arXiv Detail & Related papers (2025-01-06T17:12:19Z) - Efficient Document Ranking with Learnable Late Interactions [73.41976017860006]
Cross-Encoder (CE) and Dual-Encoder (DE) models are two fundamental approaches for query-document relevance in information retrieval.
To predict relevance, CE models use joint query-document embeddings, while DE models maintain factorized query and document embeddings.
Recently, late-interaction models have been proposed to realize more favorable latency-quality tradeoffs, by using a DE structure followed by a lightweight scorer.
arXiv Detail & Related papers (2024-06-25T22:50:48Z) - Fact Checking Beyond Training Set [64.88575826304024]
We show that the retriever-reader suffers from performance deterioration when it is trained on labeled data from one domain and used in another domain.
We propose an adversarial algorithm to make the retriever component robust against distribution shift.
We then construct eight fact checking scenarios from these datasets, and compare our model to a set of strong baseline models.
arXiv Detail & Related papers (2024-03-27T15:15:14Z) - Long Document Summarization with Top-down and Bottom-up Inference [113.29319668246407]
We propose a principled inference framework to improve summarization models on two aspects.
Our framework assumes a hierarchical latent structure of a document where the top-level captures the long range dependency.
We demonstrate the effectiveness of the proposed framework on a diverse set of summarization datasets.
arXiv Detail & Related papers (2022-03-15T01:24:51Z) - OCR-IDL: OCR Annotations for Industry Document Library Dataset [8.905920197601171]
We make public the OCR annotations for IDL documents using commercial OCR engine.
The contributed dataset (OCR-IDL) has an estimated monetary value over 20K US$.
arXiv Detail & Related papers (2022-02-25T21:30:48Z) - Donut: Document Understanding Transformer without OCR [17.397447819420695]
We propose a novel VDU model that is end-to-end trainable without underpinning OCR framework.
Our approach achieves state-of-the-art performance on various document understanding tasks in public benchmark datasets and private industrial service datasets.
arXiv Detail & Related papers (2021-11-30T18:55:19Z) - One-shot Key Information Extraction from Document with Deep Partial
Graph Matching [60.48651298832829]
Key Information Extraction (KIE) from documents improves efficiency, productivity, and security in many industrial scenarios.
Existing supervised learning methods for the KIE task need to feed a large number of labeled samples and learn separate models for different types of documents.
We propose a deep end-to-end trainable network for one-shot KIE using partial graph matching.
arXiv Detail & Related papers (2021-09-26T07:45:53Z) - Lights, Camera, Action! A Framework to Improve NLP Accuracy over OCR
documents [2.6201102730518606]
We demonstrate an effective framework for mitigating OCR errors for any downstream NLP task.
We first address the data scarcity problem for model training by constructing a document synthesis pipeline.
For the benefit of the community, we have made the document synthesis pipeline available as an open-source project.
arXiv Detail & Related papers (2021-08-06T00:32:54Z) - OCR Graph Features for Manipulation Detection in Documents [11.193867567895353]
We propose a model that leverages graph features using OCR (Optical Character Recognition)
Our model relies on a data-driven approach to detect alterations by training a random forest classifier on the graph-based OCR features.
We evaluate our algorithm's forgery detection performance on dataset constructed from real business documents with slight forgery imperfections.
arXiv Detail & Related papers (2020-09-10T21:50:45Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.