Spanish TrOCR: Leveraging Transfer Learning for Language Adaptation
- URL: http://arxiv.org/abs/2407.06950v1
- Date: Tue, 9 Jul 2024 15:31:41 GMT
- Title: Spanish TrOCR: Leveraging Transfer Learning for Language Adaptation
- Authors: Filipe Lauar, Valentin Laurent,
- Abstract summary: This study explores the transfer learning capabilities of the TrOCR architecture to Spanish.
We integrate an English TrOCR encoder with a language specific decoder and train the model on this specific language.
Fine-tuning the English TrOCR on Spanish yields superior recognition than the language specific decoder for a fixed dataset size.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This study explores the transfer learning capabilities of the TrOCR architecture to Spanish. TrOCR is a transformer-based Optical Character Recognition (OCR) model renowned for its state-of-the-art performance in English benchmarks. Inspired by Li et al. assertion regarding its adaptability to multilingual text recognition, we investigate two distinct approaches to adapt the model to a new language: integrating an English TrOCR encoder with a language specific decoder and train the model on this specific language, and fine-tuning the English base TrOCR model on a new language data. Due to the scarcity of publicly available datasets, we present a resource-efficient pipeline for creating OCR datasets in any language, along with a comprehensive benchmark of the different image generation methods employed with a focus on Visual Rich Documents (VRDs). Additionally, we offer a comparative analysis of the two approaches for the Spanish language, demonstrating that fine-tuning the English TrOCR on Spanish yields superior recognition than the language specific decoder for a fixed dataset size. We evaluate our model employing character and word error rate metrics on a public available printed dataset, comparing the performance against other open-source and cloud OCR spanish models. As far as we know, these resources represent the best open-source model for OCR in Spanish. The Spanish TrOCR models are publicly available on HuggingFace [20] and the code to generate the dataset is available on Github [25].
Related papers
- Towards Retrieval-Augmented Architectures for Image Captioning [81.11529834508424]
This work presents a novel approach towards developing image captioning models that utilize an external kNN memory to improve the generation process.
Specifically, we propose two model variants that incorporate a knowledge retriever component that is based on visual similarities.
We experimentally validate our approach on COCO and nocaps datasets and demonstrate that incorporating an explicit external memory can significantly enhance the quality of captions.
arXiv Detail & Related papers (2024-05-21T18:02:07Z) - Lost in Translation, Found in Spans: Identifying Claims in Multilingual
Social Media [40.26888469822391]
Claim span identification (CSI) is an important step in fact-checking pipelines.
Despite its importance to journalists and human fact-checkers, it remains a severely understudied problem.
We create a novel dataset, X-CLAIM, consisting of 7K real-world claims collected from numerous social media platforms in five Indian languages and English.
arXiv Detail & Related papers (2023-10-27T15:28:12Z) - EfficientOCR: An Extensible, Open-Source Package for Efficiently
Digitizing World Knowledge [1.8434042562191815]
EffOCR is a novel open-source optical character recognition (OCR) package.
It meets both the computational and sample efficiency requirements for liberating texts at scale.
EffOCR is cheap and sample efficient to train, as the model only needs to learn characters' visual appearance and not how they are used in sequence to form language.
arXiv Detail & Related papers (2023-10-16T04:20:16Z) - Learning Cross-lingual Mappings for Data Augmentation to Improve
Low-Resource Speech Recognition [31.575930914290762]
Exploiting cross-lingual resources is an effective way to compensate for data scarcity of low resource languages.
We extend the concept of learnable cross-lingual mappings for end-to-end speech recognition.
The results show that any source language ASR model can be used for a low-resource target language recognition.
arXiv Detail & Related papers (2023-06-14T15:24:31Z) - Strategies for improving low resource speech to text translation relying
on pre-trained ASR models [59.90106959717875]
This paper presents techniques and findings for improving the performance of low-resource speech to text translation (ST)
We conducted experiments on both simulated and real-low resource setups, on language pairs English - Portuguese, and Tamasheq - French respectively.
arXiv Detail & Related papers (2023-05-31T21:58:07Z) - TransDocs: Optical Character Recognition with word to word translation [2.2336243882030025]
This research work focuses on improving the optical character recognition (OCR) with ML techniques.
This work is based on ANKI dataset for English to Spanish translation.
arXiv Detail & Related papers (2023-04-15T21:40:14Z) - CLIP-ViP: Adapting Pre-trained Image-Text Model to Video-Language
Representation Alignment [146.3128011522151]
We propose a Omni Crossmodal Learning method equipped with a Video Proxy mechanism on the basis of CLIP, namely CLIP-ViP.
Our approach improves the performance of CLIP on video-text retrieval by a large margin.
Our model also achieves SOTA results on a variety of datasets, including MSR-VTT, DiDeMo, LSMDC, and ActivityNet.
arXiv Detail & Related papers (2022-09-14T05:47:02Z) - Retrieval-Augmented Transformer for Image Captioning [51.79146669195357]
We develop an image captioning approach with a kNN memory, with which knowledge can be retrieved from an external corpus to aid the generation process.
Our architecture combines a knowledge retriever based on visual similarities, a differentiable encoder, and a kNN-augmented attention layer to predict tokens.
Experimental results, conducted on the COCO dataset, demonstrate that employing an explicit external memory can aid the generation process and increase caption quality.
arXiv Detail & Related papers (2022-07-26T19:35:49Z) - OCR Improves Machine Translation for Low-Resource Languages [10.010595434359647]
We introduce and make publicly available a novel benchmark, textscOCR4MT, consisting of real and synthetic data, enriched with noise.
We evaluate state-of-the-art OCR systems on our benchmark and analyse most common errors.
We then perform an ablation study to investigate how OCR errors impact Machine Translation performance.
arXiv Detail & Related papers (2022-02-27T02:36:45Z) - Lexically Aware Semi-Supervised Learning for OCR Post-Correction [90.54336622024299]
Much of the existing linguistic data in many languages of the world is locked away in non-digitized books and documents.
Previous work has demonstrated the utility of neural post-correction methods on recognition of less-well-resourced languages.
We present a semi-supervised learning method that makes it possible to utilize raw images to improve performance.
arXiv Detail & Related papers (2021-11-04T04:39:02Z) - Reinforced Iterative Knowledge Distillation for Cross-Lingual Named
Entity Recognition [54.92161571089808]
Cross-lingual NER transfers knowledge from rich-resource language to languages with low resources.
Existing cross-lingual NER methods do not make good use of rich unlabeled data in target languages.
We develop a novel approach based on the ideas of semi-supervised learning and reinforcement learning.
arXiv Detail & Related papers (2021-06-01T05:46:22Z)
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.