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HomeArtificial IntelligenceSaying the ICDAR 2023 Competitors on Hierarchical Textual content Detection and Recognition...

Saying the ICDAR 2023 Competitors on Hierarchical Textual content Detection and Recognition – Google AI Weblog

The previous few many years have witnessed the speedy growth of Optical Character Recognition (OCR) know-how, which has developed from an educational benchmark job utilized in early breakthroughs of deep studying analysis to tangible merchandise out there in client gadgets and to third occasion builders for every day use. These OCR merchandise digitize and democratize the precious data that’s saved in paper or image-based sources (e.g., books, magazines, newspapers, kinds, road indicators, restaurant menus) in order that they are often listed, searched, translated, and additional processed by state-of-the-art pure language processing strategies.

Analysis in scene textual content detection and recognition (or scene textual content recognizing) has been the key driver of this speedy growth by means of adapting OCR to pure pictures which have extra advanced backgrounds than doc pictures. These analysis efforts, nonetheless, give attention to the detection and recognition of every particular person phrase in pictures, with out understanding how these phrases compose sentences and articles.

Format evaluation is one other related line of analysis that takes a doc picture and extracts its construction, i.e., title, paragraphs, headings, figures, tables and captions. These format evaluation efforts are parallel to OCR and have been largely developed as impartial strategies which might be usually evaluated solely on doc pictures. As such, the synergy between OCR and format evaluation stays largely under-explored. We imagine that OCR and format evaluation are mutually complementary duties that allow machine studying to interpret textual content in pictures and, when mixed, might enhance the accuracy and effectivity of each duties.

With this in thoughts, we announce the Competitors on Hierarchical Textual content Detection and Recognition (the HierText Problem), hosted as a part of the seventeenth annual Worldwide Convention on Doc Evaluation and Recognition (ICDAR 2023). The competitors is hosted on the Strong Studying Competitors web site, and represents the primary main effort to unify OCR and format evaluation. On this competitors, we invite researchers from world wide to construct methods that may produce hierarchical annotations of textual content in pictures utilizing phrases clustered into traces and paragraphs. We hope this competitors could have a big and long-term affect on image-based textual content understanding with the objective to consolidate the analysis efforts throughout OCR and format evaluation, and create new indicators for downstream data processing duties.

The idea of hierarchical textual content illustration.

Setting up a hierarchical textual content dataset

On this competitors, we use the HierText dataset that we revealed at CVPR 2022 with our paper “In the direction of Finish-to-Finish Unified Scene Textual content Detection and Format Evaluation”. It’s the primary real-image dataset that gives hierarchical annotations of textual content, containing phrase, line, and paragraph stage annotations. Right here, “phrases” are outlined as sequences of textual characters not interrupted by areas. “Strains” are then interpreted as “house“-separated clusters of “phrases” which might be logically linked in a single route, and aligned in spatial proximity. Lastly, “paragraphs” are composed of “traces” that share the identical semantic matter and are geometrically coherent.

To construct this dataset, we first annotated pictures from the Open Pictures dataset utilizing the Google Cloud Platform (GCP) Textual content Detection API. We filtered by means of these annotated pictures, maintaining solely pictures wealthy in textual content content material and format construction. Then, we labored with our third-party companions to manually appropriate all transcriptions and to label phrases, traces and paragraph composition. In consequence, we obtained 11,639 transcribed pictures, break up into three subsets: (1) a prepare set with 8,281 pictures, (2) a validation set with 1,724 pictures, and (3) a check set with 1,634 pictures. As detailed within the paper, we additionally checked the overlap between our dataset, TextOCR, and Intel OCR (each of which additionally extracted annotated pictures from Open Pictures), ensuring that the check pictures within the HierText dataset weren’t additionally included within the TextOCR or Intel OCR coaching and validation splits and vice versa. Beneath, we visualize examples utilizing the HierText dataset and display the idea of hierarchical textual content by shading every textual content entity with completely different colours. We are able to see that HierText has a range of picture area, textual content format, and excessive textual content density.

Samples from the HierText dataset. Left: Illustration of every phrase entity. Center: Illustration of line clustering. Proper: Illustration paragraph clustering.

Dataset with highest density of textual content

Along with the novel hierarchical illustration, HierText represents a brand new area of textual content pictures. We be aware that HierText is at the moment essentially the most dense publicly out there OCR dataset. Beneath we summarize the traits of HierText compared with different OCR datasets. HierText identifies 103.8 phrases per picture on common, which is greater than 3x the density of TextOCR and 25x extra dense than ICDAR-2015. This excessive density poses distinctive challenges for detection and recognition, and as a consequence HierText is used as one of many main datasets for OCR analysis at Google.

Dataset       Coaching break up       Validation break up       Testing break up       Phrases per picture      
ICDAR-2015       1,000       0       500       4.4      
TextOCR       21,778       3,124       3,232       32.1      
Intel OCR       19,1059       16,731       0       10.0      
HierText       8,281       1,724       1,634       103.8

Evaluating a number of OCR datasets to the HierText dataset.

Spatial distribution

We additionally discover that textual content within the HierText dataset has a way more even spatial distribution than different OCR datasets, together with TextOCR, Intel OCR, IC19 MLT, COCO-Textual content and IC19 LSVT. These earlier datasets are likely to have well-composed pictures, the place textual content is positioned in the midst of the photographs, and are thus simpler to determine. Quite the opposite, textual content entities in HierText are broadly distributed throughout the photographs. It is proof that our pictures are from extra numerous domains. This attribute makes HierText uniquely difficult amongst public OCR datasets.

Spatial distribution of textual content situations in several datasets.

The HierText problem

The HierText Problem represents a novel job and with distinctive challenges for OCR fashions. We invite researchers to take part on this problem and be part of us in ICDAR 2023 this 12 months in San Jose, CA. We hope this competitors will spark analysis neighborhood curiosity in OCR fashions with wealthy data representations which might be helpful for novel down-stream duties.


The core contributors to this challenge are Shangbang Lengthy, Siyang Qin, Dmitry Panteleev, Alessandro Bissacco, Yasuhisa Fujii and Michalis Raptis. Ashok Popat and Jake Walker supplied beneficial recommendation. We additionally thank Dimosthenis Karatzas and Sergi Robles from Autonomous College of Barcelona for serving to us arrange the competitors web site.



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