A significant number of the photos shared on Facebook and Instagram contain text in various forms. In the second step, we perform text recognition, where, for each of the detected regions, we use a convolutional neural network CNN to recognize and transcribe the word in the region. Our training approach involves first pre-training on SynthText and fine-tuning on human-annotated data, if available based on the language. Visit our Help Center where you can find frequently asked questions, as well as Customer Care contact information.
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Please stay in turn. Not sure which one to get? We use our own and third-party cookies to offer you the best possible service. All such crops for an image are batched into a single tensor with zero padding as needed and then processed at once by the text recognition model.
Together with these techniques, we are able to process more than a billion public images per day through our system efficiently. The extracted text is used by downstream classifiers to immediately act upon policy-violating content or by product applications like photo search.
Rosetta has been widely adopted by various products and teams within Facebook and Instagram. Text extracted from images is being used as a feature in various upstream machine learning models such as those to improve the relevance and quality of photo search, automatically identify content that violates our hate-speech policy on the platform in various languages, and improve the accuracy of classification of photos in News Feed to surface more personalized content.
But we are far from done. The rapid growth of videos as a way to share content, the need to support many more languages, and the increasing number of ways in which people share content make text extraction from images and videos an exciting challenge that helps push the frontiers of computer vision research and applications.
Text on images comes in a wide variety of forms with very little structure: Moreover, the patterns of text on images on Facebook tend to change rapidly, making this an ongoing challenge. As we look beyond images, one of the biggest challenges is extracting text efficiently from videos. The naive approach of applying image-based text extraction to every single video frame is not scalable, because of the massive growth of videos on the platform, and would only lead to wasted computational resources.
We are beginning to explore ways to apply 3D convolutions for smarter selection of video frames of interest for text extraction. Text recognition models studied in literature predominantly focus on English or Latin alphabet data sets. To support a global platform, we are also continuing to invest in extending the text recognition model for the wide number of languages used on Facebook. With a unified model for a large number of languages, we run the risk of being mediocre for each language, which makes the problem challenging.
We are therefore exploring ways to bridge the domain gap between our synthetic engine and real-world distribution of text on images. Facebook believes in building community through open source technology.
Text extraction model We perform text extraction on an image in two independent steps: Learning a region proposal network RPN , which takes that feature map as input and produces a set of proposed regions or bounding boxes that are likely to contain text, together with their confidence score.
Extracting the features from the feature map associated with the spatial extent of each candidate box, and learning a classifier to recognize them in our case, the categories are text and no text.
The proposals are sorted by their confidence scores, and non-maximum suppression NMS is used to remove duplicates or overlaps and choose the most promising proposals. Additionally, bounding box regression is typically used to improve the accuracy of the produced regions by refining the proposals. Because of this, we make two modifications to the ResNet18 architecture: We remove the global average pooling layer at the end of the model and replace the fully connected layer with a convolutional layer that can accept inputs of different lengths.
We reduce the stride of the last convolutional layers to better preserve the spatial resolution of the features. Architecture of the text recognition model. As a result, we modify our training procedure in two ways: We start training our model using only short words, with up to five characters. Once we have seen all the five-or-fewer-character words, we start training with words of six or fewer characters, then seven or fewer, etc.
This significantly simplifies the problem. Once we have reached this point, we start reducing the learning rate, as is standard practice when learning deep models. Training data Our approach for training data is a mixture of human-annotated public images with words and their locations as well as synthetic generation of text on public images. We draw inspiration from this method for our approach to synthetic engine for text extraction, which involves four steps: Inference During inference, our models are run using Caffe2 on CPU machines with a batch size of 1, owing to real-time latency constraints.
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