Sökning: "Bert Fält"
Visar resultat 1 - 5 av 6 uppsatser innehållade orden Bert Fält.
1. Exploring Machine Learning Solutions in the Context of OCR Post-Processing of Invoices
Uppsats för yrkesexamina på grundnivå, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Large corporations receive and send large volumes of invoices containing various fields detailing a transaction. Such fields include VAT, due date, total amount, etc. One common way to automatize invoice processing is optical character recognition (OCR). This technology entails automatic reading of characters from scanned images. LÄS MER
2. Image-Text context relation using Machine Learning : Research on performance of different datasets
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Based on the progress in Computer Vision and Natural Language Processing fields, Vision-Language (VL) models are designed to process information from images and texts. The thesis focused on the performance of a model, Oscar, on different datasets. LÄS MER
3. Extracting Information From PDF Invoices Using Deep Learning
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Manually extracting information from invoices can be time-consuming, especially when managing large amounts of documents. Finding a way to automatically extract this information could help businesses save resources. LÄS MER
4. Extracting Structured Data from Free-Text Clinical Notes : The impact of hierarchies in model training
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Diagnosis code assignment is a field that looks at automatically assigning diagnosis codes to free-text clinical notes. Assigning a diagnosis code to clinical notes manually needs expertise and time. Being able to do this automatically makes getting structured data from free-text clinical notes in Electronic Health Records easier. LÄS MER
5. Emotion Classification with Natural Language Processing (Comparing BERT and Bi-Directional LSTM models for use with Twitter conversations)
Master-uppsats, Lunds universitet/Matematisk statistikSammanfattning : We have constructed a novel neural network architecture called CWE-LSTM (concatenated word-emoji bidirectional long short-term memory) for classify- ing emotions in Twitter conversations. The architecture is based on a combina- tion of word and emoji embeddings with domain specificity in Twitter data. LÄS MER