OCR is inevitably linked to NLP since its final output is in text. Advances in document intelligence are driving the need for a unified technology that integrates OCR with various NLP tasks, especially semantic parsing. Since OCR and semantic parsing have been studied as separate tasks so far, the datasets for each task on their own are rich, while those for the integrated post-OCR parsing tasks are relatively insufficient. In this study, we publish a consolidated dataset for receipt parsing as the first step towards post-OCR parsing tasks. The dataset consists of thousands of Indonesian receipts, which contains images and box/text annotations for OCR, and multi-level semantic labels for parsing. The proposed dataset can be used to address various OCR and parsing tasks. Optical character recognition (OCR) is a technique for converting images of characters into digitized texts [1, 2] . Recently, deep learning in computer vision domain has significantly improved the performances of OCR [3, 4] . Nonetheless, there is still huge room for improvement, especially concerning the tasks simultaneously linked to natural language processing (NLP) as well. In particular, post-OCR parsing is currently one of the most important, yet challenging problems in both OCR and NLP community. The goal of post-OCR parsing is to predict pre-defined semantic labels from the given OCR. Researchers from both domains have long tried to tackle the problem and collected a significant amount of data sets independently. However, since it is a specialized task, the datasets contain critical limitations to provide proper supervision. The OCR datasets typically do not have parsing class labels for the extracted texts. The parsing datasets usually contain error-free and well-ordered digitized texts in contrast to the erroneous outcomes from OCR process. We can add synthetic noise to the parsing data, but the distribution and error patterns could be different from the OCR errors, which would inevitably lead to the degradation of generalization performance. Over the past few years, a few post-OCR parsing datasets have been made public through post OCR challenges [5] . For example, ICDAR 2019 Post-OCR Challenge introduced the Scanned Receipts OCR and Information Extraction (SROIE) dataset [6] . It provides receipt images of texts and two types of annotations for OCR and parsing problem: (1) box-level text annotations for OCR, and (2) document-level parse annotations for parsing. Although the availability of both OCR and parsing information have given rise to active research within the field, it still possesses some shortcomings, e.g., limited data size and lack of box-level parsing annotations. Considering that only hundreds of samples are provided in the SROIE dataset, weak document-level annotations could not provide enough supervision for training a model with satisfactory performance. In this paper, we introduce a novel dataset called CORD, which stands for a Consolidated Receipt Dataset for post-OCR parsing. To the best of our knowledge, this is the first publicly available dataset which includes both box-level text and parsing class annotations. The parsing class labels are provided in two-levels. The eight superclasses include store, payment, menu, subtotal, and total. The eight superclasses are subdivided into 54 subclasses e.g., store has nine subclasses including name, address, telephone, and fax. Furthermore, it also provides line annotations for the serialization task which is a newly emerging problem as a combination of the two tasks. Current semantic parsing techniques can handle only well-ordered texts. Texts obtained by OCR, however, are in two-dimensional space, thus we need an appropriate serialization technique for mapping obtained texts into one-dimensional space. In our experiments, serialization has a significant impact on parsing performance. To recapitulate briefly, the key contributions of our paper are as follows: • We introduce a novel and large-scale receipt dataset that can be used for OCR and parsing tasks, from task-specific to end-to-end. • Our dataset provides multi-level labels for weakly and strongly supervised parsing tasks. The dataset and descriptions will be available on https://github.com/clovaai/cord at the time of publication. 2 Data Acquisition
We introduce a large-scale receipt dataset for post-OCR parsing tasks.
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