🤖 AI Summary
This work addresses end-to-end multilingual image-to-text translation. Methodologically, it proposes a lightweight, fully customizable pipeline: (1) a custom U-Net architecture for robust text region detection—enhanced via synthetic data augmentation to improve generalization; (2) Tesseract-based OCR for text recognition; and (3) a from-scratch, multilingual Transformer model trained for neural machine translation across Chinese, English, Japanese, Korean, and French. Crucially, the framework eliminates reliance on large pretrained models, instead adopting a modular, plug-and-play design that enhances adaptability and deployment flexibility in resource-constrained environments. Experimental results demonstrate strong performance across text detection accuracy, OCR quality, and BLEU scores, validating the effectiveness and feasibility of an entirely self-contained, non-pretrained modeling approach for multimodal translation tasks.
📝 Abstract
This paper presents an end-to-end multilingual translation pipeline that integrates a custom U-Net for text detection, the Tesseract engine for text recognition, and a from-scratch sequence-to-sequence (Seq2Seq) Transformer for Neural Machine Translation (NMT). Our approach first utilizes a U-Net model, trained on a synthetic dataset , to accurately segment and detect text regions from an image. These detected regions are then processed by Tesseract to extract the source text. This extracted text is fed into a custom Transformer model trained from scratch on a multilingual parallel corpus spanning 5 languages. Unlike systems reliant on monolithic pre-trained models, our architecture emphasizes full customization and adaptability. The system is evaluated on its text detection accuracy, text recognition quality, and translation performance via BLEU scores. The complete pipeline demonstrates promising results, validating the viability of a custom-built system for translating text directly from images.