![]() Defaults to False.Īws lambda invoke -function-name textractor_simple -payload '' -Īws s3 cp s3://bucket/tracemonkey-5.txt -ĭue to the slow nature of OCR on images and AWS Lambda's 300 seconds execution limit, we used a hack (i.e., another lambda invocation) to OCR the pages of a PDF in parallel, while using S3 as our temporary store. disable_ocr (optional): Whether to disable OCR feature.text_uri (optional): A URI where the extracted text will be stored, i.e., s3://bucket/key.txt.temp_uri_prefix (optional): A URI prefix where temporary files can be stored.document_uri: A URI containing the document to extract text from, i.e., s3://bucket/key.pdf.The simple function expects an event with The speed of parsing depends on CPU and this is controlled by the amount of memory allocated to your Lambda functions.įor our needs, we find that 512MB for simple and 1024MB for ocr is a good balance between performance and cost. You can set the IAM role and other configuration options in project.json. Generally, we would advice using a specific bucket with auto-delete lifecycle rules for the temporary storage. You need to ensure your IAM role has lambda:InvokeAsync permissions, and s3:PutObject permissions on the output bucket. To deploy to AWS ( Note that the -D argument refers to dry run mode.) We use apex for our development toolchain to deploy the AWS Lambda functions the code for the two Lambda functions are found in the functions directory. The side benefit of splitting into two functions is that we can configure the memory requirements of the two functions independently. Ocr supports extracting text from images and "image" PDFs, while simple handles text extraction from the remaining formats. png) using Tesseract, andĭue to the size of code and dependencies (and AWS Lambda's 50MB package limits), the extraction system is split into two Lambda functions: simple and ocr. Microsoft PowerPoint 2007 OpenXML files (.doc) using Antiword with fallback to Catdoc, PDFs with OCR using Tesseract and Ghostscript 9.21 for PDF manipulation,.PDFs with a text layer using Poppler utilities,.Lambda-text-extractor supports many common and legacy document formats: ![]() detailed instruction for preparing libraries and dependencies necessary for processing binary documents, and.serverless architecture makes deployment quick and easy,.creation of text searchable PDFs after OCR,.scalable PDF parsing using OCR in parallel using AWS Lambda and asyncio,.out of the box support for many common binary document formats (see section on Supported Formats),.Lambda-text-extractor is a Python 3.6 app that works with the AWS Lambda architecture to extract text from common binary document formats. Extracting Text from Binary Document Formats using AWS Lambda
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