Does google cloud vision api( source path- gcsSource) supports image detection (image contains text) in PDF file?

To answer your question, yes there is a way to process images with text in PDF files. According to Google official documentation, it is normally by using OCR DOCUMENT_TEXT_DETECTION [1].

The Vision API can detect and transcribe text from PDF and TIFF files stored in Cloud Storage. Document text detection from PDF and TIFF must be requested using the files:asyncBatchAnnotate function, which performs an offline (asynchronous) request and provides its status using the operations resources. The output from a PDF/TIFF request is written to a JSON file created in the specified Cloud Storage bucket.[2]

[1]https://cloud.google.com/vision/docs/ocr#optical_character_recognition_ocr [2]https://cloud.google.com/vision/docs/pdf#vision_text_detection_pdf_gcs-gcloud

EDIT

I don't know what language you are using but I tried this python code and it processes a pdf with images without skipping them.

You need to install google-cloud-storage and google-cloud-vision.

On gcs_source_uri you have to specify your bucket name and your pdf file that you are using.

On gcs_destination_uri you only have to specify your bucket name let pdf_result as it is.

import os
import re
import json
from google.cloud import vision
from google.cloud import storage
 
"""
#pip install --upgrade google-cloud-storage
#pip install --upgrade google-cloud-vision
"""
credential_path = 'your_path'
os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = credential_path
 
client = vision.ImageAnnotatorClient()
 
batch_size = 2
mime_type = 'application/pdf'
feature = vision.Feature(
       type_=vision.Feature.Type.DOCUMENT_TEXT_DETECTION)
gcs_source_uri= 'gs://your_bucketname/your_pdf_File.pdf'
gcs_source = vision.GcsSource(uri=gcs_source_uri)
input_config = vision.InputConfig(gcs_source=gcs_source, mime_type=mime_type)
 
gcs_destination_uri = 'gs://your_bucketname/pdf_result'
gcs_destination = vision.GcsDestination(uri=gcs_destination_uri)
output_config = vision.OutputConfig(gcs_destination=gcs_destination, batch_size= batch_size)
async_request = vision.AsyncAnnotateFileRequest(
   features=[feature], input_config=input_config, output_config=output_config
)
operation = client.async_batch_annotate_files(requests=[async_request])
 
operation.result(timeout=180)
 
storage_client = storage.Client()
match = re.match(r'gs://([^/]+)/(.+)', gcs_destination_uri)
bucket_name = match.group(1)
prefix = match.group(2)
bucket = storage_client.get_bucket(bucket_name)
 
#List object with the given prefix
blob_list = list(bucket.list_blobs(prefix=prefix))
print('Output files: ')
for blob in blob_list:
   print(blob.name)
 
output = blob_list[0]
json_string = output.download_as_string()
response = json.loads(json_string)
 
first_page_response = response['responses'][0]
annotation = first_page_response['fullTextAnnotation']
 
 
print('Full text:\n')
print(annotation['text'])