PLATTER：面向印度语言手写文本识别的页面级系统

近年来，手写文本识别（Handwritten Text Recognition, HTR）领域涌现出各种新模型，声称在特定场景下均能优于其他模型。然而，由于测试集中选择不一致和多样性，导致这些模型之间进行公平比较具有挑战性。此外，近期在HTR方面的进展往往未能充分考虑印度语言等多样化语言，可能是由于相关标注数据集的稀缺所致。此外， much of the previous work has focused primarily on character-level or word-level recognition, overlooking the crucial stage of Handwritten Text Detection (HTD) necessary for building a page-level end-to-end handwritten OCR pipeline. Through our paper, we address these gaps by making three pivotal contributions. Firstly, we present an end-to-end framework for Page-Level hAndwriTTen TExt Recognition (PLATTER) by treating it as a two-stage problem involving word-level HTD followed by HTR. This approach enables us to identify, assess, and address challenges in each stage independently. Secondly, we demonstrate the usage of PLATTER to measure the performance of our language-agnostic HTD model and present a consistent comparison of six trained HTR models on ten diverse Indic languages thereby encouraging consistent comparisons. Finally, we also release a Corpus of Handwritten Indic Scripts (CHIPS), a meticulously curated, page-level Indic handwritten OCR dataset labeled for both detection and recognition purposes. Additionally, we release our code and trained models, to encourage further contributions in this direction.