Denoising can be done in several ways, with Gaussian blurring being the most popular. Denoise Input data: Data fed to the model should be properly denoised to prevent non-textual regions from being proposed as text.Increasing OCR accuracy is possible when you follow these two practices: State-of-the-art neural networks have become exceptionally good at spotting text in documents and images, even if it is slanted, rotated, or skewed. Modern OCR methods make use of text detection algorithms as a starting point.
Language Processing: NLP-based networks like RNNs and Transformers work to extract information captured in these regions and construct meaningful sentences based on features fed from the CNN layers.įully CNN-based algorithms that recognize characters directly without going through this step have been successfully explored in recent works and are especially useful to detect text that has limited temporal information to convey, like signboards or vehicle registration plates.These regions are used as attention maps and fed to language processing algorithms along with features extracted from the image. The task of the network here is similar to the Region Proposal Network in object detection algorithms like Fast-RCNN, where possible regions of interest are marked and extracted. This is achieved by using convolutional models that detect segments of text and enclose them in bounding boxes. Region Proposal: The first stage for OCR involves the detection of textual regions from the image.The bounding box data and image features are then passed onto Language Processing algorithms that use RNNs, LSTMs, and Transformers to decode the feature-based information into textual data.ĭeep learning-based OCR algorithms have two stages-the region proposal stage and the language processing stage. Generally, OCR methods include vision-based approaches used to extract textual regions and predict bounding box coordinates for the same. OCR with Deep Learningĭeep learning-based methods can efficiently extract a large number of features, making them superior to their machine learning counterparts.Īlgorithms that combine Vision and NLP-based approaches have been particularly successful in providing superior results for text recognition and detection in the wild.įurthermore, these methods provide an end-to-end detection pipeline that frees them from long-drawn pre-processing steps.
? Pro tip: Looking for the perfect OCR dataset? Check out 65+ Best Free Datasets for Machine Learning. While these work great on simple OCR datasets like easily distinguishable printed data and handwritten MNIST data, they miss out on many features, making them fail when working on complex datasets. Finally, characters building the lines are extracted, segmented, and identified via various machine learning algorithms like K-nearest neighbors and support vector machines.