PDF OCR
- 2. 2 Introduction PDF Optical Character Recognition (OCR) is the process of converting PDFs of scanned and handwritten text into machine-encoded text such that it could be further used by programs for processing and analysis.
- 3. 3 Advances in PDF OCR Solutions Modern OCRs use Neural Networks that mimic the way human brains learn. In the case of Deep-learning based OCRs, 2 genre of neural networks are applied. Convolutional Neural Networks (CNNs): CNNs are one of the most dominant sets of networks used today particularly in the realm of computer vision. It comprises multiple convolutional kernels that slide through the image to extract features. Long Short-Term Memories (LSTMs): LSTMs are a family of networks applied majorly to sequence inputs. The intuition is simple -- for any sequential data (i.e., weather, stocks), new results may be heavily dependent on previous results, and thus it would be beneficial to constantly feed- forward previous results as part of the input features in performing new predictions.
- 4. 4 Pre-processing in PDF OCRs Besides the main tasks in OCR that incorporate deep learning, many pre-processing stages to eliminate rule-based approaches are deployed. Denoising: A recent approach adopted by OCR technologies is to apply a Generative Adversarial Network (GAN) to “denoise” the input. GAN is trained from a pair of denoised and noised documents, and the goal for the generator is to generate a de-noised document as close to the ground-truth as possible. Document Identification: Knowing the type of document the OCR machine is currently processing may significantly increase the accuracy of data extraction. Recent arts have incorporated a Siamese network, or a comparison network, to compare the documents with pre-existing document formats, allowing the OCR engine to perform a document classification beforehand.
- 5. 5 Applications of PDF OCRs The main goal of a PDF OCR is to retrieve data from unstructured formats, whether that be numerical figures or text. Numerical Data Analysis: When PDFs contain numerical data, OCR helps extract them to perform statistical analysis. Specifically, OCR with the help of table or key-value pairs (KVPs) extractions can be applied to find meaningful numbers from different regions of one given text. Text Data Interpretation: Text data processing may require more stages of computation, with the ultimate goal for programs to understand the “meanings” behind words. Such a process of interpreting text data into its semantic meanings is referred to as Natural Language Processing (NLP).
- 6. 6 PDF OCR - Nanonets™ Advantage Nanonets™ PDF OCR uses deep learning and therefore is completely template and rule independent. Not only can Nanonets work on specific types of PDFs, it could also be applied onto any document type for text retrieval. Post-processing: On Nanonets™, you can post-process your data after extraction. For example, if there are any errors on the extracted data, you can write some scripts to clean the extracted data and export into desired format. Fraud Checks: If there’s any financial or confidential data in our documents, Nanonets™ models can also perform fraud checks. High Accuracy: Provides high data extraction accuracy of 95%+. The model also employs state of the art AI that improves with every document it extracts.
- 7. 7 Learn more about PDF OCRs: https://nanonets.com/blog/pdf-ocr/